e4f44b19fd
* Mark all failed ONNX layer tests as XFail
* Add additional xfailed marks
* Add one more failed tests into XFail
* Add conditions for CPU/GPU failures
* Revert "Add conditions for CPU/GPU failures"
This reverts commit 790524c59c.
* Add failures separation for CPU/GPU
* Replace all xfail with skip
257 lines
13 KiB
Python
257 lines
13 KiB
Python
# Copyright (C) 2018-2023 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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import pytest
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from common.layer_test_class import check_ir_version
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from common.onnx_layer_test_class import OnnxRuntimeLayerTest
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from openvino.tools.mo.front.common.partial_infer.utils import int64_array
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from unit_tests.utils.graph import build_graph
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def second_input_data_of_reshape(src_shape, axis):
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if axis == 0:
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return [1, -1]
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if axis == 1:
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return [0, -1]
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if axis > 1:
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return [int(np.prod(int64_array(src_shape[: axis]))), -1]
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return [-1, int(np.prod(int64_array(src_shape[len(src_shape) + axis:])))]
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def get_flatten_shape(src_shape, axis):
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flatten_axis = axis if axis >= 0 else len(src_shape) + axis
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if flatten_axis == 0:
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fst_dim = 1
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snd_dim = int(np.prod(int64_array(src_shape)))
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elif flatten_axis == 1:
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fst_dim = src_shape[0]
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snd_dim = int(np.prod(int64_array(src_shape[1:])))
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else:
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fst_dim = int(np.prod(int64_array(src_shape[: flatten_axis])))
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snd_dim = int(np.prod(int64_array(src_shape[flatten_axis:])))
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return [fst_dim, snd_dim]
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class TestLog(OnnxRuntimeLayerTest):
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def _prepare_input(self, inputs_dict):
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for input in inputs_dict.keys():
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inputs_dict[input] = np.random.rand(*(inputs_dict[input])).astype(
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np.float32) * 255 + 0.5
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return inputs_dict
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def create_net(self, shape, logsoftmax_axis, ir_version):
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"""
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ONNX net IR net
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Input->LogSoftmax->Output => Input->Softmax->Log->Output
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"""
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#
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# Create ONNX model
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#
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import onnx
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from onnx import helper
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from onnx import TensorProto
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, shape)
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, shape)
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node_def = onnx.helper.make_node(
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'LogSoftmax',
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inputs=['input'],
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outputs=['output'],
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axis=logsoftmax_axis
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)
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# Create the graph (GraphProto)
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graph_def = helper.make_graph(
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[node_def],
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'test_model',
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[input],
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[output],
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)
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# Create the model (ModelProto)
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onnx_net = helper.make_model(graph_def, producer_name='test_model')
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#
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# Create reference IR net
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#
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ref_net = None
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if check_ir_version(10, None, ir_version):
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converted_shape = shape if len(shape) != 1 else shape[0]
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flatten_shape = get_flatten_shape(shape, logsoftmax_axis)
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reshape_data_val = second_input_data_of_reshape(shape, logsoftmax_axis)
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reduce_sum_shape = np.copy(flatten_shape)
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reduce_sum_shape[1] = 1
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if len(shape) == 2 and shape == flatten_shape:
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ref_nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter', 'shape': converted_shape},
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'input_data': {'shape': shape, 'kind': 'data', 'value': None},
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'flatten_shape_val': {'shape': int64_array(reshape_data_val).shape,
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'kind': 'data',
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'value': int64_array(reshape_data_val)},
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'flatten_shape': {'type': 'Const', 'kind': 'op', 'shape': 2},
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'flatten_shape_data': {'shape': int64_array([2]), 'kind': 'data',
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'value': None},
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'reshape': {'kind': 'op', 'type': 'Reshape'},
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'reshape_data': {'kind': 'data', 'shape': flatten_shape, 'value': None},
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'reduce_max_axis_val': {'shape': int64_array([1]).shape, 'kind': 'data',
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'value': int64_array([1])},
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'reduce_max_axis': {'type': 'Const', 'kind': 'op', 'shape': 1},
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'reduce_max_axis_data': {'shape': int64_array([1]), 'kind': 'data',
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'value': None},
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'reduce_max': {'type': 'ReduceMax', 'kind': 'op', 'keep_dims': True},
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'reduce_max_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'sub_first': {'type': 'Subtract', 'kind': 'op'},
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'sub_first_data': {'shape': flatten_shape, 'kind': 'data', 'value': None},
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'reduce_sum_axis_val': {'shape': int64_array([1]).shape, 'kind': 'data',
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'value': int64_array([1])},
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'reduce_sum_axis': {'type': 'Const', 'kind': 'op', 'shape': 1},
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'reduce_sum_axis_data': {'shape': int64_array([1]), 'kind': 'data',
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'value': None},
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'reduce_sum': {'type': 'ReduceSum', 'kind': 'op', 'keep_dims': True},
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'reduce_sum_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'exp': {'type': 'Exp', 'kind': 'op'},
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'exp_data': {'shape': flatten_shape, 'kind': 'data', 'value': None},
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'log': {'type': 'Log', 'kind': 'op'},
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'log_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'sub_second': {'type': 'Subtract', 'kind': 'op'},
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'sub_second_data': {'shape': flatten_shape, 'kind': 'data', 'value': None},
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'result': {'kind': 'op', 'type': 'Result'},
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}
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ref_edges = [
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('input', 'input_data'),
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('flatten_shape_val', 'flatten_shape'),
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('flatten_shape', 'flatten_shape_data'),
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('flatten_shape_data', 'reshape', {'in': 1}),
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('input_data', 'reshape', {'in': 0}),
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('reshape', 'reshape_data'),
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('reduce_max_axis_val', 'reduce_max_axis'),
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('reduce_max_axis', 'reduce_max_axis_data'),
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('reduce_max_axis_data', 'reduce_max', {'in': 1}),
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('reduce_max', 'reduce_max_data'),
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('reshape_data', 'reduce_max', {'out': 0, 'in': 0}),
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('reshape_data', 'sub_first', {'out': 0, 'in': 0}),
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('reduce_max_data', 'sub_first', {'in': 1}),
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('sub_first', 'sub_first_data'),
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('reduce_sum_axis_val', 'reduce_sum_axis'),
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('reduce_sum_axis', 'reduce_sum_axis_data'),
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('reduce_sum_axis_data', 'reduce_sum', {'in': 1}),
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('reduce_sum', 'reduce_sum_data'),
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('sub_first_data', 'exp'),
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('exp', 'exp_data'),
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('exp_data', 'reduce_sum', {'in': 0}),
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('reduce_sum_data', 'log'),
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('log', 'log_data'),
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('log_data', 'sub_second', {'in': 1}),
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('sub_second', 'sub_second_data'),
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('sub_first_data', 'sub_second', {'out': 0, 'in': 0}),
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('sub_second_data', 'result'),
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]
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else:
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ref_nodes_attributes = {
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'input': {'kind': 'op', 'type': 'Parameter', 'shape': converted_shape},
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'input_data': {'shape': shape, 'kind': 'data', 'value': None},
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'flatten_shape_val': {'shape': int64_array(reshape_data_val).shape,
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'kind': 'data',
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'value': int64_array(reshape_data_val)},
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'flatten_shape': {'type': 'Const', 'kind': 'op', 'shape': 2},
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'flatten_shape_data': {'shape': int64_array([2]), 'kind': 'data',
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'value': None},
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'reshape': {'kind': 'op', 'type': 'Reshape'},
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'reshape_data': {'kind': 'data', 'shape': flatten_shape, 'value': None},
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'reduce_max_axis_val': {'shape': int64_array([1]).shape, 'kind': 'data',
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'value': int64_array([1])},
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'reduce_max_axis': {'type': 'Const', 'kind': 'op', 'shape': 1},
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'reduce_max_axis_data': {'shape': int64_array([1]), 'kind': 'data',
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'value': None},
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'reduce_max': {'type': 'ReduceMax', 'kind': 'op', 'keep_dims': True},
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'reduce_max_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'sub_first': {'type': 'Subtract', 'kind': 'op'},
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'sub_first_data': {'shape': flatten_shape, 'kind': 'data', 'value': None},
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'reduce_sum_axis_val': {'shape': int64_array([1]).shape, 'kind': 'data',
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'value': int64_array([1])},
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'reduce_sum_axis': {'type': 'Const', 'kind': 'op', 'shape': 1},
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'reduce_sum_axis_data': {'shape': int64_array([1]), 'kind': 'data',
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'value': None},
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'reduce_sum': {'type': 'ReduceSum', 'kind': 'op', 'keep_dims': True},
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'reduce_sum_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'exp': {'type': 'Exp', 'kind': 'op'},
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'exp_data': {'shape': flatten_shape, 'kind': 'data', 'value': None},
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'log': {'type': 'Log', 'kind': 'op'},
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'log_data': {'shape': reduce_sum_shape, 'kind': 'data', 'value': None},
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'sub_second': {'type': 'Subtract', 'kind': 'op'},
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'sub_second_data': {'shape': flatten_shape, 'kind': 'data', 'value': None},
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'last_shape_val': {'shape': int64_array(shape).shape, 'kind': 'data',
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'value': int64_array(shape)},
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'last_shape': {'type': 'Const', 'kind': 'op', 'shape': len(shape)},
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'last_shape_data': {'shape': int64_array([len(shape)]), 'kind': 'data',
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'value': None},
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'last_reshape': {'kind': 'op', 'type': 'Reshape'},
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'last_reshape_data': {'kind': 'data', 'shape': shape, 'value': None},
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'result': {'kind': 'op', 'type': 'Result'},
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}
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ref_edges = [
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('input', 'input_data'),
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('flatten_shape_val', 'flatten_shape'),
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('flatten_shape', 'flatten_shape_data'),
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('flatten_shape_data', 'reshape', {'in': 1}),
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('input_data', 'reshape', {'in': 0}),
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('reshape', 'reshape_data'),
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('reduce_max_axis_val', 'reduce_max_axis'),
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('reduce_max_axis', 'reduce_max_axis_data'),
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('reduce_max_axis_data', 'reduce_max', {'in': 1}),
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('reduce_max', 'reduce_max_data'),
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('reshape_data', 'reduce_max', {'out': 0, 'in': 0}),
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('reshape_data', 'sub_first', {'out': 0, 'in': 0}),
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('reduce_max_data', 'sub_first', {'in': 1}),
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('sub_first', 'sub_first_data'),
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('reduce_sum_axis_val', 'reduce_sum_axis'),
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('reduce_sum_axis', 'reduce_sum_axis_data'),
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('reduce_sum_axis_data', 'reduce_sum', {'in': 1}),
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('reduce_sum', 'reduce_sum_data'),
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('sub_first_data', 'exp'),
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('exp', 'exp_data'),
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('exp_data', 'reduce_sum', {'in': 0}),
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('reduce_sum_data', 'log'),
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('log', 'log_data'),
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('log_data', 'sub_second', {'in': 1}),
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('sub_second', 'sub_second_data'),
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('sub_first_data', 'sub_second', {'out': 0, 'in': 0}),
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('last_shape_val', 'last_shape'),
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('last_shape', 'last_shape_data'),
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('last_shape_data', 'last_reshape', {'in': 1}),
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('sub_second_data', 'last_reshape', {'in': 0}),
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('last_reshape', 'last_reshape_data'),
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('last_reshape_data', 'result'),
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]
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ref_net = build_graph(ref_nodes_attributes, ref_edges)
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return onnx_net, ref_net
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test_data_precommit = [
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dict(shape=[2, 4], logsoftmax_axis=-1),
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dict(shape=[2, 3, 2, 5, 6], logsoftmax_axis=-2)]
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test_data = [
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dict(shape=[10, 12], logsoftmax_axis=-1),
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dict(shape=[4, 5, 3], logsoftmax_axis=1),
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dict(shape=[6, 8, 5, 7], logsoftmax_axis=2),
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dict(shape=[2, 3, 2, 5, 6], logsoftmax_axis=-2)]
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@pytest.mark.parametrize("params", test_data)
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@pytest.mark.nightly
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def test_log(self, params, ie_device, precision, ir_version, temp_dir, use_old_api):
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if ie_device == 'GPU':
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pytest.skip('GREEN_SUITE')
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self._test(*self.create_net(**params, ir_version=ir_version), ie_device, precision,
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ir_version,
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temp_dir=temp_dir, use_old_api=use_old_api)
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