# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ import pytest import numpy as np import mindspore import mindspore.nn as nn import mindspore.context as context from mindspore import Tensor from mindspore.ops.composite import GradOperation @pytest.mark.level0 @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_mirror_pad(): context.set_context(mode=context.GRAPH_MODE, device_target="GPU") test1_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]] test_1_paddings = ((0, 0), (0, 0), (1, 1), (2, 2)) test1_arr_exp = [[[[6, 5, 4, 5, 6, 5, 4], [3, 2, 1, 2, 3, 2, 1], [6, 5, 4, 5, 6, 5, 4], [9, 8, 7, 8, 9, 8, 7], [6, 5, 4, 5, 6, 5, 4]]]] test2_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]] test_2_paddings = ((0, 0), (0, 0), (1, 1), (2, 2)) test2_arr_exp = [[[[2, 1, 1, 2, 3, 3, 2], [2, 1, 1, 2, 3, 3, 2], [5, 4, 4, 5, 6, 6, 5], [8, 7, 7, 8, 9, 9, 8], [8, 7, 7, 8, 9, 9, 8]]]] reflectOp = nn.Pad(mode='REFLECT', paddings=test_1_paddings) symmOp = nn.Pad(mode='SYMMETRIC', paddings=test_2_paddings) x_test_1 = Tensor(np.array(test1_arr_in), dtype=mindspore.float32) x_test_2 = Tensor(np.array(test2_arr_in), dtype=mindspore.float32) y_test_1 = reflectOp(x_test_1).asnumpy() y_test_2 = symmOp(x_test_2).asnumpy() print(np.array(test1_arr_in)) print(y_test_1) np.testing.assert_equal(np.array(test1_arr_exp), y_test_1) np.testing.assert_equal(np.array(test2_arr_exp), y_test_2) class Grad(nn.Cell): def __init__(self, network): super(Grad, self).__init__() self.grad = GradOperation(get_all=True, sens_param=True) self.network = network def construct(self, input_, output_grad): return self.grad(self.network)(input_, output_grad) class Net(nn.Cell): def __init__(self, pads, mode_): super(Net, self).__init__() self.pad = nn.Pad(mode=mode_, paddings=pads) def construct(self, x): return self.pad(x) @pytest.mark.level0 @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_mirror_pad_backprop(): context.set_context(mode=context.GRAPH_MODE, device_target="GPU") test_arr_in = [[[[1, 2, 3], [4, 5, 6], [7, 8, 9]]]] # size -> 3*3 test_arr_in = Tensor(test_arr_in, dtype=mindspore.float32) dy = (np.ones((1, 1, 4, 5)) * 0.1).astype(np.float32) expected_dx = np.array([[[[0.2, 0.2, 0.1], [0.4, 0.4, 0.2], [0.2, 0.2, 0.1]]]]) net = Grad(Net(((0, 0), (0, 0), (1, 0), (0, 2)), "REFLECT")) dx = net(test_arr_in, Tensor(dy)) dx = dx[0].asnumpy() np.testing.assert_array_almost_equal(dx, expected_dx) @pytest.mark.level0 @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_mirror_pad_fwd_back_4d_int32_reflect(): context.set_context(mode=context.GRAPH_MODE, device_target="GPU") # set constants shape = (2, 3, 3, 5) pads = ((1, 0), (2, 0), (1, 2), (3, 4)) total_val = np.prod(shape) test_arr_np = np.arange(total_val).reshape(shape) + 1 test_arr_ms = Tensor(test_arr_np, dtype=mindspore.int32) # fwd_pass_check op = nn.Pad(mode="REFLECT", paddings=pads) expected_np_result = np.pad(test_arr_np, pads, 'reflect') obtained_ms_res = op(test_arr_ms).asnumpy() np.testing.assert_array_equal(expected_np_result, obtained_ms_res) # backwards pass check GradNet = Grad(Net(pads, "REFLECT")) dy_value = Tensor(np.ones(obtained_ms_res.shape), dtype=mindspore.int32) dx_value_obtained = GradNet(test_arr_ms, dy_value)[0].asnumpy() dx_value_expected = np.array([[[[4, 6, 6, 6, 2], [6, 9, 9, 9, 3], [2, 3, 3, 3, 1]], [[8, 12, 12, 12, 4], [12, 18, 18, 18, 6], [4, 6, 6, 6, 2]], [[8, 12, 12, 12, 4], [12, 18, 18, 18, 6], [4, 6, 6, 6, 2]]], [[[8, 12, 12, 12, 4], [12, 18, 18, 18, 6], [4, 6, 6, 6, 2]], [[16, 24, 24, 24, 8], [24, 36, 36, 36, 12], [8, 12, 12, 12, 4]], [[16, 24, 24, 24, 8], [24, 36, 36, 36, 12], [8, 12, 12, 12, 4]]]], dtype=np.int32) np.testing.assert_array_equal(dx_value_expected, dx_value_obtained) @pytest.mark.level0 @pytest.mark.platform_x86_gpu_training @pytest.mark.env_onecard def test_mirror_pad_fwd_back_4d_int32_symm(): context.set_context(mode=context.GRAPH_MODE, device_target="GPU") # set constants shape = (2, 3, 3, 5) pads = ((1, 0), (2, 0), (1, 2), (3, 4)) total_val = np.prod(shape) test_arr_np = np.arange(total_val).reshape(shape) + 1 test_arr_ms = Tensor(test_arr_np, dtype=mindspore.int32) # fwd_pass_check op = nn.Pad(mode="SYMMETRIC", paddings=pads) expected_np_result = np.pad(test_arr_np, pads, 'symmetric') obtained_ms_res = op(test_arr_ms).asnumpy() np.testing.assert_array_equal(expected_np_result, obtained_ms_res) # backwards pass check GradNet = Grad(Net(pads, "SYMMETRIC")) dy_value = Tensor(np.ones(obtained_ms_res.shape), dtype=mindspore.int32) dx_value_obtained = GradNet(test_arr_ms, dy_value)[0].asnumpy() dx_value_expected = np.array([[[[16, 24, 24, 16, 16], [16, 24, 24, 16, 16], [16, 24, 24, 16, 16]], [[16, 24, 24, 16, 16], [16, 24, 24, 16, 16], [16, 24, 24, 16, 16]], [[8, 12, 12, 8, 8], [8, 12, 12, 8, 8], [8, 12, 12, 8, 8]]], [[[8, 12, 12, 8, 8], [8, 12, 12, 8, 8], [8, 12, 12, 8, 8]], [[8, 12, 12, 8, 8], [8, 12, 12, 8, 8], [8, 12, 12, 8, 8]], [[4, 6, 6, 4, 4], [4, 6, 6, 4, 4], [4, 6, 6, 4, 4]]]], dtype=np.int32) np.testing.assert_array_equal(dx_value_expected, dx_value_obtained)