1# Copyright 2020 Huawei Technologies Co., Ltd 2# 3# Licensed under the Apache License, Version 2.0 (the "License"); 4# you may not use this file except in compliance with the License. 5# You may obtain a copy of the License at 6# 7# http://www.apache.org/licenses/LICENSE-2.0 8# 9# Unless required by applicable law or agreed to in writing, software 10# distributed under the License is distributed on an "AS IS" BASIS, 11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12# See the License for the specific language governing permissions and 13# limitations under the License. 14# ============================================================================ 15 16import numpy as np 17import pytest 18 19from mindspore import Tensor 20from mindspore.ops import operations as P 21from mindspore.ops.operations import _inner_ops as inner 22import mindspore.nn as nn 23import mindspore.context as context 24 25class DynamicShapeNet(nn.Cell): 26 def __init__(self): 27 super(DynamicShapeNet, self).__init__() 28 self.convert_to_dynamic_shape_op = inner.GpuConvertToDynamicShape() 29 self.dynamic_shape_op = P.DynamicShape() 30 31 def construct(self, x): 32 x_dynamic_shape = self.convert_to_dynamic_shape_op(x) 33 return self.dynamic_shape_op(x_dynamic_shape) 34 35 36def dynamic_shape(np_type): 37 context.set_context(mode=context.GRAPH_MODE, device_target="GPU") 38 39 dynamic_shape_net = DynamicShapeNet() 40 41 shape = (1,) 42 x = Tensor(np.zeros(shape).astype(np_type)) 43 ms_out = dynamic_shape_net(x).asnumpy() 44 expected = np.array(shape) 45 np.testing.assert_array_equal(ms_out, expected) 46 47 shape = (7,) 48 x = Tensor(np.zeros(shape).astype(np_type)) 49 ms_out = dynamic_shape_net(x).asnumpy() 50 expected = np.array(shape) 51 np.testing.assert_array_equal(ms_out, expected) 52 53 shape = (1, 1) 54 x = Tensor(np.zeros(shape).astype(np_type)) 55 ms_out = dynamic_shape_net(x).asnumpy() 56 expected = np.array(shape) 57 np.testing.assert_array_equal(ms_out, expected) 58 59 shape = (1, 7) 60 x = Tensor(np.zeros(shape).astype(np_type)) 61 ms_out = dynamic_shape_net(x).asnumpy() 62 expected = np.array(shape) 63 np.testing.assert_array_equal(ms_out, expected) 64 65 shape = (3, 1) 66 x = Tensor(np.zeros(shape).astype(np_type)) 67 ms_out = dynamic_shape_net(x).asnumpy() 68 expected = np.array(shape) 69 np.testing.assert_array_equal(ms_out, expected) 70 71 shape = (2, 4) 72 x = Tensor(np.zeros(shape).astype(np_type)) 73 ms_out = dynamic_shape_net(x).asnumpy() 74 expected = np.array(shape) 75 np.testing.assert_array_equal(ms_out, expected) 76 77 shape = (1, 1, 1) 78 x = Tensor(np.zeros(shape).astype(np_type)) 79 ms_out = dynamic_shape_net(x).asnumpy() 80 expected = np.array(shape) 81 np.testing.assert_array_equal(ms_out, expected) 82 83 shape = (1, 5, 3) 84 x = Tensor(np.zeros(shape).astype(np_type)) 85 ms_out = dynamic_shape_net(x).asnumpy() 86 expected = np.array(shape) 87 np.testing.assert_array_equal(ms_out, expected) 88 89 shape = (2, 3, 1, 3, 1) 90 x = Tensor(np.zeros(shape).astype(np_type)) 91 ms_out = dynamic_shape_net(x).asnumpy() 92 expected = np.array(shape) 93 np.testing.assert_array_equal(ms_out, expected) 94 95@pytest.mark.level1 96@pytest.mark.platform_x86_gpu_training 97@pytest.mark.env_onecard 98def test_dynamic_shape_int32(): 99 dynamic_shape(np.int32) 100 101@pytest.mark.level0 102@pytest.mark.platform_x86_gpu_training 103@pytest.mark.env_onecard 104def test_dynamic_shape_float16(): 105 dynamic_shape(np.float16) 106 107@pytest.mark.level0 108@pytest.mark.platform_x86_gpu_training 109@pytest.mark.env_onecard 110def test_dynamic_shape_float32(): 111 dynamic_shape(np.float32) 112 113@pytest.mark.level1 114@pytest.mark.platform_x86_gpu_training 115@pytest.mark.env_onecard 116def test_dynamic_shape_bool(): 117 dynamic_shape(np.bool) 118