def test_concat(): element_type = Type.f32 A = Parameter(element_type, Shape([1, 2])) B = Parameter(element_type, Shape([1, 2])) C = Parameter(element_type, Shape([1, 2])) parameter_list = [A, B, C] axis = 0 function = Function([ng.concat([A, B, C], axis)], parameter_list, 'test') backend = Backend.create(test.BACKEND_NAME) a = backend.create_tensor(element_type, Shape([1, 2])) b = backend.create_tensor(element_type, Shape([1, 2])) c = backend.create_tensor(element_type, Shape([1, 2])) result = backend.create_tensor(element_type, Shape([3, 2])) a.write(util.numpy_to_c(np.array([1, 2], dtype=np.float32)), 8) b.write(util.numpy_to_c(np.array([5, 6], dtype=np.float32)), 8) c.write(util.numpy_to_c(np.array([7, 8], dtype=np.float32)), 8) result_arr = np.zeros(6, dtype=np.float32).reshape(3, 2) result.write(util.numpy_to_c(result_arr), 24) handle = backend.compile(function) handle.call([result], [a, b, c]) result.read(util.numpy_to_c(result_arr), 24) a_arr = np.array([[1, 2]], dtype=np.float32) b_arr = np.array([[5, 6]], dtype=np.float32) c_arr = np.array([[7, 8]], dtype=np.float32) result_arr_ref = np.concatenate((a_arr, b_arr, c_arr), axis) assert np.allclose(result_arr, result_arr_ref)
def test_concat(): a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6]]) axis = 0 expected = np.concatenate((a, b), axis=0) runtime = get_runtime() parameter_a = ng.parameter(list(a.shape), name="A", dtype=np.float32) parameter_b = ng.parameter(list(b.shape), name="B", dtype=np.float32) node = ng.concat([parameter_a, parameter_b], axis) computation = runtime.computation(node, parameter_a, parameter_b) result = computation(a, b) assert np.allclose(result, expected)
def test_concat(): a = np.array([[1, 2], [3, 4]]) b = np.array([[5, 6]]) axis = 0 expected = np.concatenate((a, b), axis=0) runtime = get_runtime() parameter_a = ng.parameter(list(a.shape), name='A', dtype=np.float32) parameter_b = ng.parameter(list(b.shape), name='B', dtype=np.float32) node = ng.concat([parameter_a, parameter_b], axis) computation = runtime.computation(node, parameter_a, parameter_b) result = computation(a, b) assert np.allclose(result, expected)
def Concat(onnx_node, ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode """Concatenate a list of tensors into a single tensor.""" axis = onnx_node.get_attribute_value('axis') if axis is None: raise ValueError('Concat node (%s): requires "axis" attribute', onnx_node.name) if len(ng_inputs) < 2: raise ValueError('Concat node (%s): requires at least 2 inputs, %d given.', onnx_node.name, len(ng_inputs)) unique_input_ranks = {len(node.shape) for node in ng_inputs} if len(unique_input_ranks) != 1: raise ValueError('Concat node (%s): input tensors must be of equal rank.', onnx_node.name) if axis >= unique_input_ranks.pop() or axis < 0: raise ValueError('Concat node (%s): `axis` attribute is out of range.', onnx_node.name) return ng.concat(ng_inputs, axis)
def test_concat(): element_type = Type.f32 A = Parameter(element_type, Shape([1, 2])) B = Parameter(element_type, Shape([1, 2])) C = Parameter(element_type, Shape([1, 2])) parameter_list = [A, B, C] axis = 0 function = Function([ng.concat([A, B, C], axis)], parameter_list, "test") a_arr = np.array([[1, 2]], dtype=np.float32) b_arr = np.array([[5, 6]], dtype=np.float32) c_arr = np.array([[7, 8]], dtype=np.float32) runtime = get_runtime() computation = runtime.computation(function, *parameter_list) result = computation(a_arr, b_arr, c_arr)[0] expected = np.concatenate((a_arr, b_arr, c_arr), axis) assert np.allclose(result, expected)
def make_convolution_op(onnx_node, ng_inputs): # type: (NodeWrapper, List[NgraphNode]) -> NgraphNode """ Create an ngraph convolution Op based on an ONNX node. :param onnx_node: wrapped ONNX node for Conv of ConvTranspose op :param ng_inputs: ngraph TensorOp input tensors :return: ngraph Op for convolution or deconvolution """ if len(ng_inputs) == 3: data, weights, bias = ng_inputs elif len(ng_inputs) == 2: data, weights = ng_inputs bias = ng.constant(0, dtype=get_dtype(data.get_element_type())) else: raise ValueError( 'Conv node (%s): unexpected number of input values: %d.', onnx_node.name, len(ng_inputs)) groups = onnx_node.get_attribute_value('group', 1) strides = get_strides(onnx_node) dilation = get_dilations(onnx_node) padding_below, padding_above = get_pads(onnx_node) if groups != 1: # Split one convolution op to N ops where N is the number of groups and concat results after computation. # reference: https://github.com/NervanaSystems/ngraph-mxnet/blob/fdd692/src/ngraph/ngraph_emitter.cc#L822-L856 data_shape = list(data.shape) weights_shape = list(weights.shape) convolutions_nodes = [] # initial bounds for splice data_lower_part = len(data_shape) * [0] data_upper_part = copy(data_shape) weights_lower_part = len(weights_shape) * [0] weights_upper_part = copy(weights_shape) for group in range(groups): # update bounds for splice data_lower_part[1] = group * int((data_shape[1] / groups)) data_upper_part[1] = (group + 1) * int((data_shape[1] / groups)) sliced_data = ng.slice(data, data_lower_part, data_upper_part) # update bounds for splice weights_lower_part[0] = group * int((weights_shape[0] / groups)) weights_upper_part[0] = max((group + 1) * int( (weights_shape[0] / groups)), 1) sliced_weights = ng.slice(weights, weights_lower_part, weights_upper_part) convolutions_nodes.append( ng.convolution(sliced_data, sliced_weights, strides, dilation, padding_below, padding_above)) conv = ng.concat(convolutions_nodes, axis=1) else: conv = ng.convolution(data, weights, strides, dilation, padding_below, padding_above) if len(bias.shape) > 0: return conv + ng.broadcast_to(bias, conv.shape, 1) else: return conv