From 49b2cf5feee66010c6598f8d4fc49f1fc1f29048 Mon Sep 17 00:00:00 2001 From: chenweihang Date: Wed, 4 Jul 2018 09:44:39 +0000 Subject: [PATCH] adjust some code based reviewer's advice --- paddle/fluid/operators/unsqueeze_op.cc | 30 ++- .../tests/unittests/test_unsqueeze_op.py | 216 ++++-------------- 2 files changed, 60 insertions(+), 186 deletions(-) diff --git a/paddle/fluid/operators/unsqueeze_op.cc b/paddle/fluid/operators/unsqueeze_op.cc index c50398867..62e45468a 100644 --- a/paddle/fluid/operators/unsqueeze_op.cc +++ b/paddle/fluid/operators/unsqueeze_op.cc @@ -1,4 +1,4 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. +/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. @@ -36,15 +36,13 @@ class UnsqueezeOpInferShape : public framework::InferShapeBase { PADDLE_ENFORCE(static_cast(x_dims.size()) <= 6, "Invalid dimensions, dynamic dimensions should within " "[1, 6] dimensions (Eigen limit)."); - // Validity Check: the range of unsqueeze aixs. - for (int axis : axes) { - PADDLE_ENFORCE(axis < 6, - "Invalid dimensions, input axis should within " - "[1, 6] dimensions (Eigen limit)."); - } - auto out_dims = GetOutputShape(axes, x_dims); ctx->SetOutputDim("Out", out_dims); + if (x_dims[0] == out_dims[0]) { + // Only pass LoD when the first dimension of output and Input(X) + // are the same. + ctx->ShareLoD("X", "Out"); + } } static framework::DDim GetOutputShape(const std::vector unsqz_dims, @@ -102,6 +100,8 @@ class UnsqueezeOp : public framework::OperatorBase { auto &axes = Attr>("axes"); auto x_dims = scope.FindVar(Input("X"))->Get().dims(); auto out_dims = UnsqueezeOpInferShape::GetOutputShape(axes, x_dims); + // auto out_dims = + // scope.FindVar(Output("Out"))->Get().dims(); framework::AttributeMap attrs; attrs["shape"] = framework::vectorize2int(out_dims); @@ -121,7 +121,19 @@ class UnsqueezeOpMaker : public framework::OpProtoAndCheckerMaker { AddOutput("Out", "(Tensor). The output tensor of unsqueeze operator."); AddAttr>("axes", "(std::vector). List of positive integers," - " indicate the dimensions to be inserted"); + " indicate the dimensions to be inserted") + .AddCustomChecker([](const std::vector &axes) { + // Validity Check: axes dims (<6). + PADDLE_ENFORCE(static_cast(axes.size()) < 6, + "Invalid dimensions, dynamic dimensions should within " + "[1, 6] dimensions (Eigen limit)."); + // Validity Check: the range of unsqueeze aixs. + for (int axis : axes) { + PADDLE_ENFORCE(axis < 6, + "Invalid dimensions, input axis should within " + "[1, 6] dimensions (Eigen limit)."); + } + }); AddAttr( "inplace", "(default: false) Unsqueeze the source tensor's shape without " diff --git a/python/paddle/fluid/tests/unittests/test_unsqueeze_op.py b/python/paddle/fluid/tests/unittests/test_unsqueeze_op.py index eff90f461..62dc6fcb9 100644 --- a/python/paddle/fluid/tests/unittests/test_unsqueeze_op.py +++ b/python/paddle/fluid/tests/unittests/test_unsqueeze_op.py @@ -21,14 +21,11 @@ from op_test import OpTest # Correct: General. class TestUnsqueezeOp(OpTest): def setUp(self): - ori_shape = (3, 5) - axes = (0, 2) - new_shape = (1, 3, 1, 5) - + self.init_test_case() self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": False} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} + self.inputs = {"X": np.random.random(self.ori_shape).astype("float32")} + self.attrs = {"axes": self.axes, "inplace": False} + self.outputs = {"Out": self.inputs["X"].reshape(self.new_shape)} def test_check_output(self): self.check_output() @@ -36,194 +33,59 @@ class TestUnsqueezeOp(OpTest): def test_check_grad(self): self.check_grad(["X"], "Out") + def init_test_case(self): + self.ori_shape = (3, 5) + self.axes = (1, 2) + self.new_shape = (3, 1, 1, 5) -# Correct: Single input index. -class TestUnsqueezeOp1(OpTest): - def setUp(self): - ori_shape = (3, 5) - axes = (-1, ) - new_shape = (3, 5, 1) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": False} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - def test_check_grad(self): - self.check_grad(["X"], "Out") +# Correct: Single input index. +class TestUnsqueezeOp1(TestUnsqueezeOp): + def init_test_case(self): + self.ori_shape = (3, 5) + self.axes = (-1, ) + self.new_shape = (3, 5, 1) # Correct: Mixed input axis. -class TestUnsqueezeOp2(OpTest): - def setUp(self): - ori_shape = (3, 5) - axes = (0, -1) - new_shape = (1, 3, 5, 1) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": False} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") +class TestUnsqueezeOp2(TestUnsqueezeOp): + def init_test_case(self): + self.ori_shape = (3, 5) + self.axes = (0, -1) + self.new_shape = (1, 3, 5, 1) # Correct: There is duplicated axis. -class TestUnsqueezeOp3(OpTest): - def setUp(self): - ori_shape = (3, 2, 5) - axes = (0, 3, 3) - new_shape = (1, 3, 2, 1, 1, 5) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": False} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") +class TestUnsqueezeOp3(TestUnsqueezeOp): + def init_test_case(self): + self.ori_shape = (3, 2, 5) + self.axes = (0, 3, 3) + self.new_shape = (1, 3, 2, 1, 1, 5) # Correct: Inplace. -class TestUnsqueezeOpInplace1(OpTest): - def setUp(self): - ori_shape = (3, 5) - axes = (0, 2) - new_shape = (1, 3, 1, 5) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": True} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") +class TestUnsqueezeOpInplace1(TestUnsqueezeOp): + def init_test_case(self): + self.ori_shape = (3, 5) + self.axes = (0, 2) + self.new_shape = (1, 3, 1, 5) # Correct: Inplace. There is mins index. -class TestUnsqueezeOpInplace2(OpTest): - def setUp(self): - ori_shape = (3, 5) - axes = (0, -2) - new_shape = (1, 3, 1, 5) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": True} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") +class TestUnsqueezeOpInplace2(TestUnsqueezeOp): + def init_test_case(self): + self.ori_shape = (3, 5) + self.axes = (0, -2) + self.new_shape = (1, 3, 1, 5) # Correct: Inplace. There is duplicated axis. -class TestUnsqueezeOpInplace3(OpTest): - def setUp(self): - ori_shape = (3, 2, 5) - axes = (0, 3, 3) - new_shape = (1, 3, 2, 1, 1, 5) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": True} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") - - -''' -# Error: Output dimension is error. -class TestUnsqueezeOp4(OpTest): - def setUp(self): - ori_shape = (3, 5) - axes = (0, 3) - new_shape = (1, 3, 1, 1, 5) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": False} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") - -# Error: Input axis is large than output range. -class TestUnsqueezeOp5(OpTest): - def setUp(self): - ori_shape = (3, 5) - axes = (0, 4) - new_shape = (1, 3, 5, 1) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": False} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} +class TestUnsqueezeOpInplace3(TestUnsqueezeOp): + def init_test_case(self): + self.ori_shape = (3, 2, 5) + self.axes = (0, 3, 3) + self.new_shape = (1, 3, 2, 1, 1, 5) - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") - -# Error: Input axes is large than Eigen limit. -class TestUnsqueezeOp6(OpTest): - def setUp(self): - ori_shape = (3, 5) - axes = (0, 2, 10) - new_shape = (1, 3, 1, 5, 1) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": False} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") - -# Error: Input axes size is large than Eigen limit. -class TestUnsqueezeOp7(OpTest): - def setUp(self): - ori_shape = (3, 5) - axes = (0, 2, 2, 2, 2, 2) - new_shape = (1, 3, 1, 1, 5, 1) - - self.op_type = "unsqueeze" - self.inputs = {"X": np.random.random(ori_shape).astype("float32")} - self.attrs = {"axes": axes, "inplace": False} - self.outputs = {"Out": self.inputs["X"].reshape(new_shape)} - - def test_check_output(self): - self.check_output() - - def test_check_grad(self): - self.check_grad(["X"], "Out") -''' if __name__ == "__main__": unittest.main() -- GitLab