[MO|nGraph]GatherND_8 (#7743)
* Add GatherND_8 operation * Update shape infer function and tests * Initial commit for nGraph GatherND_8 operation * Add GatherNDBase class implementation * Fix base class errors * Add missrd header * Update base class * Update GatherND_8 implementation * Fix codestyle * Fix wrong rank * Implement tests for gatherND_8 shape inference function * fix codestyle * Add limitation to doc * Siplyfy check in shape inference * Add more test cases * Update shape inference function * Add more test cases to cover all case with dynamic input shapes * Update shape inference function * Refactor tests * Add visitor tests for gatherND_8 operation * Correct comment * Add additional check is shape inference function * Update shape inference implementation for gathernd operartion * Fix codestyle * Remove restriction for data is fully defined * Update shape inference functon * Fix missed check for nonetype * Remove redundant checks for batch_dims * Fix codestyle
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Anton Chetverikov authored and 
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@@ -16,7 +16,7 @@ class GatherND(Op):
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mandatory_props = {
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'type': self.op,
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'op': self.op,
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'version': 'opset5',
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'version': 'opset8',
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'infer': self.infer,
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'in_ports_count': 2,
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'out_ports_count': 1,
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@@ -56,41 +56,55 @@ class GatherND(Op):
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assert len(indices_shape) > 0, "Indices must not be a scalar"
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assert (batch_dims + indices_shape[-1]) <= len(data_shape), \
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"Length of a tuple with indices must not exceed a rank of data tensor excluding batch dimensions"
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assert node['version'] in ['opset5', 'opset8'], 'Unsupported version of GatherND operation: {}, operation ' \
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'name : {}'.format(node['version'], node.soft_get('name'))
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# compute output shape
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batch = []
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if batch_dims > 0:
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if is_fully_defined(data_shape[:batch_dims]):
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batch = [np.prod(data_shape[:batch_dims]).tolist()]
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else:
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batch = [dynamic_dimension_value]
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else:
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batch = []
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if node['version'] == 'opset5': # Support old version of gatherND shape inference
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if is_fully_defined(data_shape[:batch_dims]):
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batch = [np.prod(data_shape[:batch_dims]).tolist()]
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else:
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batch = [dynamic_dimension_value]
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elif node['version'] == 'opset8':
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for dim in range(batch_dims):
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assert compatible_dims(indices_shape[dim], data_shape[dim]),\
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"Batch dimensions in data.shape and indices.shape must be compatible"
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if is_fully_defined(indices_shape[:batch_dims]):
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batch = indices_shape[:batch_dims].tolist()
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elif is_fully_defined(data_shape[:batch_dims]):
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batch = data_shape[:batch_dims].tolist()
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else:
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for ind in range(batch_dims):
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if indices_shape[ind] != dynamic_dimension_value:
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batch.append(indices_shape[ind])
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elif data_shape[ind] != dynamic_dimension_value:
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batch.append(data_shape[ind])
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else:
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batch.append(dynamic_dimension_value)
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slice_shape = list(data_shape[(batch_dims + indices_shape[-1]):])
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output_shape = batch + list(indices_shape[batch_dims:-1]) + slice_shape
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output_shape = batch + list(indices_shape)[batch_dims:-1] + slice_shape
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node.out_port(0).data.set_shape(output_shape)
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# compute output value if all input values are defined
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if is_fully_defined(indices_value) and is_fully_defined(data_value):
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output_value = np.zeros(output_shape, dtype=data_value.dtype)
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if batch_dims == 0:
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output_indices_range = int64_array(indices_shape[:-1])
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for output_index in np.ndindex(tuple(output_indices_range)):
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indices_tuple = indices_value[output_index]
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output_value[output_index] = data_value[tuple(indices_tuple.T)]
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else:
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batch_dims_range = int64_array(indices_shape[:batch_dims])
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for batch_indices in np.ndindex(tuple(batch_dims_range)):
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# compute batch index in output tensor
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batch_ind = 0
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num_elements = 1
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for ind in reversed(range(len(batch_dims_range))):
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batch_ind += batch_indices[ind] * num_elements
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num_elements *= batch_dims_range[ind]
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output_indices_range = int64_array(indices_shape[batch_dims:-1])
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for output_index in np.ndindex(tuple(output_indices_range)):
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tmp_ind = batch_indices + output_index
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indices_tuple = tuple(indices_value[tmp_ind].T)
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full_input_ind = batch_indices + indices_tuple
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full_output_ind = tuple(np.array([batch_ind]).T) + output_index
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output_value[full_output_ind] = data_value[full_input_ind]
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# compute output value if all input indices are defined
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if is_fully_defined(indices_value) and data_value is not None:
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batch_dims_size = 1
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for i in range(batch_dims):
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batch_dims_size *= indices_shape[i]
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output_data = []
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reshaped_indices = indices_value.reshape(batch_dims_size, -1, indices_shape[-1])
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reshaped_data = data_value.reshape((batch_dims_size,) + tuple((data_shape[batch_dims:])))
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for batch_dim in range(reshaped_indices.shape[0]):
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for outer_dim in range(reshaped_indices.shape[1]):
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gather_index = tuple(reshaped_indices[batch_dim][outer_dim])
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output_data.append(reshaped_data[(batch_dim,) + gather_index])
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output_value = np.asarray(output_data, dtype=data_value.dtype).reshape(output_shape)
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node.out_port(0).data.set_value(output_value)
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@@ -14,7 +14,7 @@ nodes_attributes = {'data': {'kind': 'op'},
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'data_data': {'shape': None, 'value': None, 'kind': 'data'},
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'indices': {'kind': 'op'},
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'indices_data': {'shape': None, 'value': None, 'kind': 'data'},
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'gathernd_node': {'op': 'GatherNDUpdate', 'kind': 'op', 'batch_dims': 0},
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'gathernd_node': {'op': 'GatherNDUpdate', 'kind': 'op', 'batch_dims': 0, 'version': 'opset8'},
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'output': {'shape': None, 'value': None, 'kind': 'data'}}
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# graph 1
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@@ -25,17 +25,21 @@ edges = [('data', 'data_data', {'in': 0}),
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('gathernd_node', 'output', {'out': 0})]
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# test data for partial infer: gather elements
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inputs1 = {'data_data': {'shape': int64_array([10, 40]), 'value': None},
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inputs = {'data_data': {'shape': int64_array([10, 40]), 'value': None},
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'indices_data': {'shape': int64_array([3, 2]), 'value': None}}
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# test data for partial infer: gather slices
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inputs2 = {'data_data': {'shape': int64_array([10, 40, 30]), 'value': None},
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inputs1 = {'data_data': {'shape': int64_array([10, 40, 30]), 'value': None},
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'indices_data': {'shape': int64_array([3, 2]), 'value': None}}
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# test data for partial infer: gather slices and batch_dims=2
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inputs3 = {'data_data': {'shape': int64_array([10, 40, 4, 9]), 'value': None},
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inputs2 = {'data_data': {'shape': int64_array([10, 40, 4, 9]), 'value': None},
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'indices_data': {'shape': int64_array([10, 40, 3, 5, 1]), 'value': None}}
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# test data for partial infer: gather slices and batch_dims=3 and indices.shape[-1]=len(data.shape)-batch_dims
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inputs3 = {'data_data': {'shape': int64_array([1, 64, 64, 320]), 'value': None},
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'indices_data': {'shape': int64_array([1, 64, 64, 1, 1]), 'value': None}}
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# test data for constant folding: gather elements, batch_dims = 0
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inputs4 = {'data_data': {'shape': int64_array([2, 2]), 'value': int64_array([[1, 2],
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[3, 4]])},
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@@ -110,6 +114,14 @@ output8 = int64_array([[3, 8, 6],
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inputs9 = {'data_data': {'shape': shape_array([dynamic_dimension_value, 40, 4, 9]), 'value': None},
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'indices_data': {'shape': shape_array([dynamic_dimension_value, 40, 3, 5, 1]), 'value': None}}
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# test data for partial infer: gather slices and batch_dims=2
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inputs10 = {'data_data': {'shape': shape_array([40, dynamic_dimension_value, 4, 9]), 'value': None},
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'indices_data': {'shape': shape_array([40, dynamic_dimension_value, 3, 5, 1]), 'value': None}}
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# test data for partial infer: gather slices and batch_dims=2
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inputs11 = {'data_data': {'shape': shape_array([dynamic_dimension_value, 40, 4, 9]), 'value': None},
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'indices_data': {'shape': shape_array([40, dynamic_dimension_value, 3, 5, 1]), 'value': None}}
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# invalid test case with incorrect rank for indices
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inputs_inv1 = {'data_data': {'shape': int64_array([10, 40]), 'value': None},
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'indices_data': {'shape': int64_array([5, 3, 4]), 'value': None}}
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@@ -123,12 +135,13 @@ inputs_inv3 = {'data_data': {'shape': int64_array([10, 40, 20, 10, 2]), 'value':
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'indices_data': {'shape': int64_array([10, 40, 4]), 'value': None}}
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class TestGatherNDUpdate(unittest.TestCase):
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class TestGatherND_5(unittest.TestCase):
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def setUp(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 0
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nodes_attributes['gathernd_node']['version'] = 'opset5'
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def test_partial_infer_gather_element(self):
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graph = build_graph(nodes_attributes, edges, inputs1)
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graph = build_graph(nodes_attributes, edges, inputs)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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@@ -142,7 +155,7 @@ class TestGatherNDUpdate(unittest.TestCase):
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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def test_partial_infer_gather_slice(self):
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graph = build_graph(nodes_attributes, edges, inputs2)
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graph = build_graph(nodes_attributes, edges, inputs1)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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@@ -157,7 +170,7 @@ class TestGatherNDUpdate(unittest.TestCase):
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def test_partial_infer_gather_slice_batch_dims2(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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graph = build_graph(nodes_attributes, edges, inputs3)
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graph = build_graph(nodes_attributes, edges, inputs2)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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@@ -170,7 +183,22 @@ class TestGatherNDUpdate(unittest.TestCase):
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self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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def test_partial_infer_gather_slice_batch_dims2_dynamic(self):
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def test_partial_infer_gather_slice_batch_dims3(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 3
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graph = build_graph(nodes_attributes, edges, inputs3)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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# prepare reference results
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ref_output_shape = int64_array([4096, 1])
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# get the result
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res_output_shape = graph.node['output']['shape']
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self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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def test_partial_infer_gather_slice_batch_dims2_dynamic1(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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graph = build_graph(nodes_attributes, edges, inputs9)
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gathernd_node = Node(graph, 'gathernd_node')
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@@ -185,6 +213,36 @@ class TestGatherNDUpdate(unittest.TestCase):
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self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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def test_partial_infer_gather_slice_batch_dims2_dynamic2(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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graph = build_graph(nodes_attributes, edges, inputs10)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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# prepare reference results
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ref_output_shape = shape_array([dynamic_dimension_value, 3, 5, 9])
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# get the result
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res_output_shape = graph.node['output']['shape']
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self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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def test_partial_infer_gather_slice_batch_dims2_dynamic3(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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graph = build_graph(nodes_attributes, edges, inputs11)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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# prepare reference results
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ref_output_shape = shape_array([dynamic_dimension_value, 3, 5, 9])
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# get the result
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res_output_shape = graph.node['output']['shape']
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self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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def test_infer4(self):
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graph = build_graph(nodes_attributes, edges, inputs4)
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gathernd_node = Node(graph, 'gathernd_node')
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@@ -205,7 +263,7 @@ class TestGatherNDUpdate(unittest.TestCase):
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res_output_value = graph.node['output']['value']
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self.assertTrue(np.array_equal(output5, res_output_value),
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'values do not match expected: {} and given: {}'.format(output4, res_output_value))
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'values do not match expected: {} and given: {}'.format(output5, res_output_value))
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def test_infer6(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 1
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@@ -217,7 +275,7 @@ class TestGatherNDUpdate(unittest.TestCase):
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res_output_value = graph.node['output']['value']
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self.assertTrue(np.array_equal(output6, res_output_value),
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'values do not match expected: {} and given: {}'.format(output4, res_output_value))
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'values do not match expected: {} and given: {}'.format(output6, res_output_value))
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def test_infer7(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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@@ -228,8 +286,9 @@ class TestGatherNDUpdate(unittest.TestCase):
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# get the result
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res_output_value = graph.node['output']['value']
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self.assertTrue(np.array_equal(output7, res_output_value),
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'values do not match expected: {} and given: {}'.format(output4, res_output_value))
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output = output7.reshape([6, 1])
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self.assertTrue(np.array_equal(output, res_output_value),
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'values do not match expected: {} and given: {}'.format(output, res_output_value))
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def test_infer8(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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@@ -241,7 +300,32 @@ class TestGatherNDUpdate(unittest.TestCase):
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res_output_value = graph.node['output']['value']
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self.assertTrue(np.array_equal(output8, res_output_value),
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'values do not match expected: {} and given: {}'.format(output4, res_output_value))
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'values do not match expected: {} and given: {}'.format(output8, res_output_value))
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def test_infer9(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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graph = build_graph(nodes_attributes, edges, inputs8)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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# get the result
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res_output_value = graph.node['output']['value']
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self.assertTrue(np.array_equal(output8, res_output_value),
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'values do not match expected: {} and given: {}'.format(output8, res_output_value))
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def test_infer9_opset_5(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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graph = build_graph(nodes_attributes, edges, inputs8)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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# get the result
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res_output_value = graph.node['output']['value']
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output = output8.reshape([6, 3])
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self.assertTrue(np.array_equal(output, res_output_value),
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'values do not match expected: {} and given: {}'.format(output, res_output_value))
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def test_infer_invalid1(self):
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graph = build_graph(nodes_attributes, edges, inputs_inv1)
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@@ -259,3 +343,114 @@ class TestGatherNDUpdate(unittest.TestCase):
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graph = build_graph(nodes_attributes, edges, inputs_inv3)
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gathernd_node = Node(graph, 'gathernd_node')
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self.assertRaises(AssertionError, GatherND.infer, gathernd_node)
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def test_partial_infer_gather_slice_batch_dims2_opset8(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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nodes_attributes['gathernd_node']['version'] = 'opset8'
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graph = build_graph(nodes_attributes, edges, inputs2)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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# prepare reference results
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ref_output_shape = int64_array([10, 40, 3, 5, 9])
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# get the result
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res_output_shape = graph.node['output']['shape']
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self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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def test_partial_infer_gather_slice_batch_dims3_opset8(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 3
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nodes_attributes['gathernd_node']['version'] = 'opset8'
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graph = build_graph(nodes_attributes, edges, inputs3)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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# prepare reference results
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ref_output_shape = int64_array([1, 64, 64, 1])
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# get the result
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res_output_shape = graph.node['output']['shape']
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self.assertTrue(np.array_equal(ref_output_shape, res_output_shape),
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'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
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def test_partial_infer_gather_slice_batch_dims2_dynamic1_opset8(self):
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nodes_attributes['gathernd_node']['batch_dims'] = 2
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nodes_attributes['gathernd_node']['version'] = 'opset8'
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graph = build_graph(nodes_attributes, edges, inputs9)
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gathernd_node = Node(graph, 'gathernd_node')
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GatherND.infer(gathernd_node)
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# prepare reference results
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ref_output_shape = shape_array([dynamic_dimension_value, 40, 3, 5, 9])
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# get the result
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res_output_shape = graph.node['output']['shape']
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self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
|
||||
'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
|
||||
|
||||
def test_partial_infer_gather_slice_batch_dims2_dynamic2_opset8(self):
|
||||
nodes_attributes['gathernd_node']['batch_dims'] = 2
|
||||
nodes_attributes['gathernd_node']['version'] = 'opset8'
|
||||
graph = build_graph(nodes_attributes, edges, inputs10)
|
||||
gathernd_node = Node(graph, 'gathernd_node')
|
||||
GatherND.infer(gathernd_node)
|
||||
|
||||
# prepare reference results
|
||||
ref_output_shape = shape_array([40, dynamic_dimension_value, 3, 5, 9])
|
||||
|
||||
# get the result
|
||||
res_output_shape = graph.node['output']['shape']
|
||||
|
||||
self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
|
||||
'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
|
||||
|
||||
def test_partial_infer_gather_slice_batch_dims2_dynamic3_opset8(self):
|
||||
nodes_attributes['gathernd_node']['batch_dims'] = 2
|
||||
nodes_attributes['gathernd_node']['version'] = 'opset8'
|
||||
graph = build_graph(nodes_attributes, edges, inputs11)
|
||||
gathernd_node = Node(graph, 'gathernd_node')
|
||||
GatherND.infer(gathernd_node)
|
||||
|
||||
# prepare reference results
|
||||
ref_output_shape = shape_array([40, 40, 3, 5, 9])
|
||||
|
||||
# get the result
|
||||
res_output_shape = graph.node['output']['shape']
|
||||
|
||||
self.assertTrue(strict_compare_tensors(ref_output_shape, res_output_shape),
|
||||
'values do not match expected: {} and given: {}'.format(ref_output_shape, res_output_shape))
|
||||
|
||||
def test_infer7_opset8(self):
|
||||
nodes_attributes['gathernd_node']['batch_dims'] = 2
|
||||
nodes_attributes['gathernd_node']['version'] = 'opset8'
|
||||
graph = build_graph(nodes_attributes, edges, inputs7)
|
||||
gathernd_node = Node(graph, 'gathernd_node')
|
||||
GatherND.infer(gathernd_node)
|
||||
|
||||
# get the result
|
||||
res_output_value = graph.node['output']['value']
|
||||
|
||||
output = output7.reshape([2, 3, 1])
|
||||
|
||||
self.assertTrue(np.array_equal(output, res_output_value),
|
||||
'values do not match expected: {} and given: {}'.format(output, res_output_value))
|
||||
|
||||
def test_infer8_opset8(self):
|
||||
nodes_attributes['gathernd_node']['batch_dims'] = 2
|
||||
nodes_attributes['gathernd_node']['version'] = 'opset8'
|
||||
graph = build_graph(nodes_attributes, edges, inputs8)
|
||||
gathernd_node = Node(graph, 'gathernd_node')
|
||||
GatherND.infer(gathernd_node)
|
||||
|
||||
# get the result
|
||||
res_output_value = graph.node['output']['value']
|
||||
|
||||
output = output8.reshape([2, 3, 3])
|
||||
|
||||
self.assertTrue(np.array_equal(output, res_output_value),
|
||||
'values do not match expected: {} and given: {}'.format(output, res_output_value))
|
||||
Reference in new issue
Block a user