adjust some code based reviewer's advice

paddle/fluid/operators/unsqueeze_op.cc

-/* 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<int>(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<int> unsqz_dims,
@@ -102,6 +100,8 @@ class UnsqueezeOp : public framework::OperatorBase {
     auto &axes = Attr<std::vector<int>>("axes");
     auto x_dims = scope.FindVar(Input("X"))->Get<framework::LoDTensor>().dims();
     auto out_dims = UnsqueezeOpInferShape::GetOutputShape(axes, x_dims);
+    // auto out_dims =
+    // scope.FindVar(Output("Out"))->Get<framework::LoDTensor>().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<std::vector<int>>("axes",
                               "(std::vector<int>). List of positive integers,"
-                              " indicate the dimensions to be inserted");
+                              " indicate the dimensions to be inserted")
+        .AddCustomChecker([](const std::vector<int> &axes) {
+          // Validity Check: axes dims (<6).
+          PADDLE_ENFORCE(static_cast<int>(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<bool>(
         "inplace",
         "(default: false) Unsqueeze the source tensor's shape without "

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)
+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)
 
-        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)}
-
-    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()