From 83441c660b5865a75d12df3cd4dcd730e26af9b7 Mon Sep 17 00:00:00 2001 From: Zhang Ting Date: Mon, 14 Oct 2019 10:28:38 +0800 Subject: [PATCH] [cherry-pick] fixed group_norm's bug and modified unittest, test=release/1.6 (#20506) (#20591) * modified group_norm's unittest for pass statement, test=develop * fix group_norm's bug: scale or bias is None which causes segmentation fault, test=develop --- paddle/fluid/operators/group_norm_op.h | 16 ++++++++++------ .../tests/unittests/test_group_norm_op.py | 19 ++++++++++--------- 2 files changed, 20 insertions(+), 15 deletions(-) diff --git a/paddle/fluid/operators/group_norm_op.h b/paddle/fluid/operators/group_norm_op.h index d4a1b3f036b..afe70ea64a9 100644 --- a/paddle/fluid/operators/group_norm_op.h +++ b/paddle/fluid/operators/group_norm_op.h @@ -220,7 +220,8 @@ class GroupNormGradKernel : public framework::OpKernel { if (bias_data) val -= bias_data[gid * group_size + cid]; T dval = iter_y_data[0]; dp_scale += val * dval; - dp_bias += dval * scale_data[gid * group_size + cid]; + if (scale_data) + dp_bias += dval * scale_data[gid * group_size + cid]; if (scale_data && scale_data[gid * group_size + cid] != 0) val /= scale_data[gid * group_size + cid]; @@ -237,8 +238,9 @@ class GroupNormGradKernel : public framework::OpKernel { T dly = tmp_y[0]; T dss = dp_scale; T dbs = dp_bias; - T v_scale = scale_data[gid * group_size + cid]; - T v_bias = bias_data[gid * group_size + cid]; + T v_scale = 1., v_bias = 0.; + if (scale_data) v_scale = scale_data[gid * group_size + cid]; + if (bias_data) v_bias = bias_data[gid * group_size + cid]; v_y -= v_bias; if (v_scale != 0) v_y /= v_scale; iter_d_x_data[0] = @@ -256,7 +258,8 @@ class GroupNormGradKernel : public framework::OpKernel { if (bias_data) val -= bias_data[gid * group_size + cid]; T dval = iter_y_data[0]; dp_scale += val * dval; - dp_bias += dval * scale_data[gid * group_size + cid]; + if (scale_data) + dp_bias += dval * scale_data[gid * group_size + cid]; if (scale_data && scale_data[gid * group_size + cid] != 0) val /= scale_data[gid * group_size + cid]; @@ -276,8 +279,9 @@ class GroupNormGradKernel : public framework::OpKernel { T dly = tmp_y[0]; T dss = dp_scale; T dbs = dp_bias; - T v_scale = scale_data[gid * group_size + cid]; - T v_bias = bias_data[gid * group_size + cid]; + T v_scale = 1.0, v_bias = 0.; + if (scale_data) v_scale = scale_data[gid * group_size + cid]; + if (bias_data) v_bias = bias_data[gid * group_size + cid]; v_y -= v_bias; if (v_scale != 0) v_y /= v_scale; iter_d_x_data[0] = diff --git a/python/paddle/fluid/tests/unittests/test_group_norm_op.py b/python/paddle/fluid/tests/unittests/test_group_norm_op.py index 7fcde530fe9..e2b4e146406 100644 --- a/python/paddle/fluid/tests/unittests/test_group_norm_op.py +++ b/python/paddle/fluid/tests/unittests/test_group_norm_op.py @@ -195,12 +195,10 @@ class TestGroupNormOpLargeData_With_NHWC(TestGroupNormOp): class TestGroupNormAPI_With_NHWC(OpTest): def test_case1(self): - data1 = fluid.layers.data( - name='data1', shape=[3, 3, 4], dtype='float32') + data1 = fluid.data(name='data1', shape=[None, 3, 3, 4], dtype='float32') out1 = fluid.layers.group_norm( input=data1, groups=2, data_layout="NHWC") - data2 = fluid.layers.data( - name='data2', shape=[4, 3, 3], dtype='float32') + data2 = fluid.data(name='data2', shape=[None, 4, 3, 3], dtype='float32') out2 = fluid.layers.group_norm( input=data2, groups=2, data_layout="NCHW") @@ -223,14 +221,17 @@ class TestGroupNormAPI_With_NHWC(OpTest): self.assertTrue(np.allclose(results[0], expect_res1[0])) self.assertTrue(np.allclose(results[1], expect_res2[0])) + +class TestGroupNormException(OpTest): # data_layout is not NHWC or NCHW - def test_case2(self): - data = fluid.layers.data(name='data', shape=[3, 3, 4], dtype="float32") - try: + def test_exception(self): + data = fluid.data(name='data', shape=[None, 3, 3, 4], dtype="float32") + + def attr_data_format(): out = fluid.layers.group_norm( input=data, groups=2, data_layout="NDHW") - except: - pass + + self.assertRaises(ValueError, attr_data_format) if __name__ == '__main__': -- GitLab