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The following commit(s) were added to refs/heads/master by this push: new 8f5930b fix normalize mean error bug (#15539) 8f5930b is described below commit 8f5930b2c95a6b7594ff6535a097e35b3315bc6d Author: nicklhy <nick...@gmail.com> AuthorDate: Sun Jul 21 06:10:36 2019 +0800 fix normalize mean error bug (#15539) * fix normalize mean error bug * add scalar mean/std tests for image_normalize --- src/operator/image/image_random-inl.h | 2 +- tests/python/unittest/test_operator.py | 53 ++++++++++++++++++++++++++++++++-- 2 files changed, 52 insertions(+), 3 deletions(-) diff --git a/src/operator/image/image_random-inl.h b/src/operator/image/image_random-inl.h index aeb189f..e00b255 100644 --- a/src/operator/image/image_random-inl.h +++ b/src/operator/image/image_random-inl.h @@ -339,7 +339,7 @@ void NormalizeOpForward(const nnvm::NodeAttrs &attrs, std::vector<float> mean(3); std::vector<float> std(3); if (param.mean.ndim() == 1) { - mean[0] = mean[1] = mean[3] = param.mean[0]; + mean[0] = mean[1] = mean[2] = param.mean[0]; } else { mean[0] = param.mean[0]; mean[1] = param.mean[1]; diff --git a/tests/python/unittest/test_operator.py b/tests/python/unittest/test_operator.py index fea07f5..915a83f 100644 --- a/tests/python/unittest/test_operator.py +++ b/tests/python/unittest/test_operator.py @@ -8678,7 +8678,7 @@ def test_invalid_max_pooling_pad_type_same(): @with_seed() def test_image_normalize(): - # Part 1 - Test 3D Input + # Part 1 - Test 3D input with 3D mean/std shape_3d = (3, 28, 28) mean = (0, 1, 2) std = (3, 2, 1) @@ -8709,7 +8709,7 @@ def test_image_normalize(): # check backward using finite difference check_numeric_gradient(img_norm_sym, [data_in_3d], atol=0.001) - # Part 2 - Test 4D Input + # Part 2 - Test 4D input with 3D mean/std shape_4d = (2, 3, 28, 28) data_in_4d = mx.nd.random.uniform(0, 1, shape_4d) @@ -8741,6 +8741,55 @@ def test_image_normalize(): # check backward using finite difference check_numeric_gradient(img_norm_sym, [data_in_4d], atol=0.001) + # Part 3 - Test 3D input with scalar mean/std + shape_3d = (3, 28, 28) + mean = 1.0 + std = 2.0 + + data_in_3d = mx.nd.random.uniform(0, 1, shape_3d) + data_expected_3d = data_in_3d.asnumpy() + data_expected_3d[:][:][:] = (data_expected_3d[:][:][:] - 1.0) / 2.0 + + data = mx.symbol.Variable('data') + img_norm_sym = mx.sym.image.normalize(data=data, mean=mean, std=std) + + # check forward + check_symbolic_forward(img_norm_sym, [data_in_3d], [data_expected_3d], + rtol=1e-5, atol=1e-5) + + # Gradient is 1/std_dev + grad_expected_3d = np.ones(shape_3d) + grad_expected_3d[:][:][:] = 1 / 2.0 + + # check backward + check_symbolic_backward(img_norm_sym, location=[data_in_3d], out_grads=[mx.nd.ones(shape_3d)], + expected=[grad_expected_3d], rtol=1e-5, atol=1e-5) + + # check backward using finite difference + check_numeric_gradient(img_norm_sym, [data_in_3d], atol=0.001) + + # Part 4 - Test 4D input with scalar mean/std + shape_4d = (2, 3, 28, 28) + + data_in_4d = mx.nd.random.uniform(0, 1, shape_4d) + data_expected_4d = data_in_4d.asnumpy() + data_expected_4d[:][:][:][:] = (data_expected_4d[:][:][:][:] - 1.0) / 2.0 + + # check forward + check_symbolic_forward(img_norm_sym, [data_in_4d], [data_expected_4d], + rtol=1e-5, atol=1e-5) + + # Gradient is 1/std_dev + grad_expected_4d = np.ones(shape_4d) + grad_expected_4d[:][:][:][:] = 1 / 2.0 + + # check backward + check_symbolic_backward(img_norm_sym, location=[data_in_4d], out_grads=[mx.nd.ones(shape_4d)], + expected=[grad_expected_4d], rtol=1e-5, atol=1e-5) + + # check backward using finite difference + check_numeric_gradient(img_norm_sym, [data_in_4d], atol=0.001) + @with_seed() def test_index_array(): def test_index_array_default():