def create_diff_if_with_two_outputs(condition_val):
condition = ng.constant(condition_val, dtype=np.bool)
# then_body
X_t = ng.parameter([2], np.float32, "X")
Y_t = ng.parameter([2], np.float32, "Y")
mmul_t = ng.matmul(X_t, Y_t, False, False)
mul_t = ng.multiply(Y_t, X_t)
then_body_res_1 = ng.result(mmul_t)
then_body_res_2 = ng.result(mul_t)
then_body = GraphBody([X_t, Y_t], [then_body_res_1, then_body_res_2])
then_body_inputs = [
TensorIteratorInvariantInputDesc(1, 0),
TensorIteratorInvariantInputDesc(2, 1)
]
then_body_outputs = [
TensorIteratorBodyOutputDesc(0, 0),
TensorIteratorBodyOutputDesc(1, 1)
]
# else_body
X_e = ng.parameter([2], np.float32, "X")
Z_e = ng.parameter([], np.float32, "Z")
mul_e = ng.multiply(X_e, Z_e)
else_body_res_1 = ng.result(Z_e)
else_body_res_2 = ng.result(mul_e)
else_body = GraphBody([X_e, Z_e], [else_body_res_1, else_body_res_2])
else_body_inputs = [
TensorIteratorInvariantInputDesc(1, 0),
TensorIteratorInvariantInputDesc(3, 1)
]
else_body_outputs = [
TensorIteratorBodyOutputDesc(0, 0),
TensorIteratorBodyOutputDesc(1, 1)
]
X = ng.constant([3, 4], dtype=np.float32)
Y = ng.constant([2, 1], dtype=np.float32)
Z = ng.constant(4.0, dtype=np.float32)
if_node = ng.if_op(condition, [X, Y, Z], (then_body, else_body),
(then_body_inputs, else_body_inputs),
(then_body_outputs, else_body_outputs))
return if_node
def create_ngraph_function(args) -> Function:
weights = np.fromfile(args.model, dtype=np.float32)
weights_offset = 0
padding_begin = [0, 0]
padding_end = [0, 0]
# input
input_shape = [64, 1, 28, 28]
param_node = ngraph.parameter(input_shape, np.float32, 'Parameter')
# convolution 1
conv_1_kernel_shape, conv_1_kernel_length = shape_and_length([20, 1, 5, 5])
conv_1_kernel = ngraph.constant(
weights[0:conv_1_kernel_length].reshape(conv_1_kernel_shape))
weights_offset += conv_1_kernel_length
conv_1_node = ngraph.convolution(param_node, conv_1_kernel, [1, 1],
padding_begin, padding_end, [1, 1])
# add 1
add_1_kernel_shape, add_1_kernel_length = shape_and_length([1, 20, 1, 1])
add_1_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
add_1_kernel_length].reshape(add_1_kernel_shape))
weights_offset += add_1_kernel_length
add_1_node = ngraph.add(conv_1_node, add_1_kernel)
# maxpool 1
maxpool_1_node = ngraph.max_pool(add_1_node, [2, 2], padding_begin,
padding_end, [2, 2], 'ceil', None)
# convolution 2
conv_2_kernel_shape, conv_2_kernel_length = shape_and_length(
[50, 20, 5, 5])
conv_2_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
conv_2_kernel_length].reshape(conv_2_kernel_shape))
weights_offset += conv_2_kernel_length
conv_2_node = ngraph.convolution(maxpool_1_node, conv_2_kernel, [1, 1],
padding_begin, padding_end, [1, 1])
# add 2
add_2_kernel_shape, add_2_kernel_length = shape_and_length([1, 50, 1, 1])
add_2_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
add_2_kernel_length].reshape(add_2_kernel_shape))
weights_offset += add_2_kernel_length
add_2_node = ngraph.add(conv_2_node, add_2_kernel)
# maxpool 2
maxpool_2_node = ngraph.max_pool(add_2_node, [2, 2], padding_begin,
padding_end, [2, 2], 'ceil', None)
# reshape 1
reshape_1_dims, reshape_1_length = shape_and_length([2])
# workaround to get int64 weights from float32 ndarray w/o unnecessary copying
dtype_weights = np.frombuffer(weights[weights_offset:weights_offset +
2 * reshape_1_length],
dtype=np.int64)
reshape_1_kernel = ngraph.constant(dtype_weights)
weights_offset += 2 * reshape_1_length
reshape_1_node = ngraph.reshape(maxpool_2_node, reshape_1_kernel, True)
# matmul 1
matmul_1_kernel_shape, matmul_1_kernel_length = shape_and_length(
[500, 800])
matmul_1_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
matmul_1_kernel_length].reshape(matmul_1_kernel_shape))
weights_offset += matmul_1_kernel_length
matmul_1_node = ngraph.matmul(reshape_1_node, matmul_1_kernel, False, True)
# add 3
add_3_kernel_shape, add_3_kernel_length = shape_and_length([1, 500])
add_3_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
add_3_kernel_length].reshape(add_3_kernel_shape))
weights_offset += add_3_kernel_length
add_3_node = ngraph.add(matmul_1_node, add_3_kernel)
# ReLU
relu_node = ngraph.relu(add_3_node)
# reshape 2
reshape_2_kernel = ngraph.constant(dtype_weights)
reshape_2_node = ngraph.reshape(relu_node, reshape_2_kernel, True)
# matmul 2
matmul_2_kernel_shape, matmul_2_kernel_length = shape_and_length([10, 500])
matmul_2_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
matmul_2_kernel_length].reshape(matmul_2_kernel_shape))
weights_offset += matmul_2_kernel_length
matmul_2_node = ngraph.matmul(reshape_2_node, matmul_2_kernel, False, True)
# add 4
add_4_kernel_shape, add_4_kernel_length = shape_and_length([1, 10])
add_4_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
add_4_kernel_length].reshape(add_4_kernel_shape))
weights_offset += add_4_kernel_length
add_4_node = ngraph.add(matmul_2_node, add_4_kernel)
# softmax
softmax_axis = 1
softmax_node = ngraph.softmax(add_4_node, softmax_axis)
# result
result_node = ngraph.result(softmax_node)
# nGraph function
function = Function(result_node, [param_node], 'lenet')
return function
def create_ngraph_function(args: argparse.Namespace) -> ngraph.impl.Function:
"""Create a network on the fly from the source code using ngraph"""
def shape_and_length(shape: list) -> typing.Tuple[list, int]:
length = reduce(lambda x, y: x * y, shape)
return shape, length
weights = np.fromfile(args.model, dtype=np.float32)
weights_offset = 0
padding_begin = padding_end = [0, 0]
# input
input_shape = [64, 1, 28, 28]
param_node = ngraph.parameter(input_shape, np.float32, 'Parameter')
# convolution 1
conv_1_kernel_shape, conv_1_kernel_length = shape_and_length([20, 1, 5, 5])
conv_1_kernel = ngraph.constant(
weights[0:conv_1_kernel_length].reshape(conv_1_kernel_shape))
weights_offset += conv_1_kernel_length
conv_1_node = ngraph.convolution(param_node, conv_1_kernel, [1, 1],
padding_begin, padding_end, [1, 1])
# add 1
add_1_kernel_shape, add_1_kernel_length = shape_and_length([1, 20, 1, 1])
add_1_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
add_1_kernel_length].reshape(add_1_kernel_shape), )
weights_offset += add_1_kernel_length
add_1_node = ngraph.add(conv_1_node, add_1_kernel)
# maxpool 1
maxpool_1_node = ngraph.max_pool(add_1_node, [2, 2], padding_begin,
padding_end, [2, 2], 'ceil', None)
# convolution 2
conv_2_kernel_shape, conv_2_kernel_length = shape_and_length(
[50, 20, 5, 5])
conv_2_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
conv_2_kernel_length].reshape(conv_2_kernel_shape), )
weights_offset += conv_2_kernel_length
conv_2_node = ngraph.convolution(maxpool_1_node, conv_2_kernel, [1, 1],
padding_begin, padding_end, [1, 1])
# add 2
add_2_kernel_shape, add_2_kernel_length = shape_and_length([1, 50, 1, 1])
add_2_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
add_2_kernel_length].reshape(add_2_kernel_shape), )
weights_offset += add_2_kernel_length
add_2_node = ngraph.add(conv_2_node, add_2_kernel)
# maxpool 2
maxpool_2_node = ngraph.max_pool(add_2_node, [2, 2], padding_begin,
padding_end, [2, 2], 'ceil', None)
# reshape 1
reshape_1_dims, reshape_1_length = shape_and_length([2])
# workaround to get int64 weights from float32 ndarray w/o unnecessary copying
dtype_weights = np.frombuffer(
weights[weights_offset:weights_offset + 2 * reshape_1_length],
dtype=np.int64,
)
reshape_1_kernel = ngraph.constant(dtype_weights)
weights_offset += 2 * reshape_1_length
reshape_1_node = ngraph.reshape(maxpool_2_node, reshape_1_kernel, True)
# matmul 1
matmul_1_kernel_shape, matmul_1_kernel_length = shape_and_length(
[500, 800])
matmul_1_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
matmul_1_kernel_length].reshape(matmul_1_kernel_shape), )
weights_offset += matmul_1_kernel_length
matmul_1_node = ngraph.matmul(reshape_1_node, matmul_1_kernel, False, True)
# add 3
add_3_kernel_shape, add_3_kernel_length = shape_and_length([1, 500])
add_3_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
add_3_kernel_length].reshape(add_3_kernel_shape), )
weights_offset += add_3_kernel_length
add_3_node = ngraph.add(matmul_1_node, add_3_kernel)
# ReLU
relu_node = ngraph.relu(add_3_node)
# reshape 2
reshape_2_kernel = ngraph.constant(dtype_weights)
reshape_2_node = ngraph.reshape(relu_node, reshape_2_kernel, True)
# matmul 2
matmul_2_kernel_shape, matmul_2_kernel_length = shape_and_length([10, 500])
matmul_2_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
matmul_2_kernel_length].reshape(matmul_2_kernel_shape), )
weights_offset += matmul_2_kernel_length
matmul_2_node = ngraph.matmul(reshape_2_node, matmul_2_kernel, False, True)
# add 4
add_4_kernel_shape, add_4_kernel_length = shape_and_length([1, 10])
add_4_kernel = ngraph.constant(
weights[weights_offset:weights_offset +
add_4_kernel_length].reshape(add_4_kernel_shape), )
weights_offset += add_4_kernel_length
add_4_node = ngraph.add(matmul_2_node, add_4_kernel)
# softmax
softmax_axis = 1
softmax_node = ngraph.softmax(add_4_node, softmax_axis)
# result
result_node = ngraph.result(softmax_node)
return ngraph.impl.Function(result_node, [param_node], 'lenet')