API/OP (center_loss, fluid.one_hot, prroi_pool, roi_pool, ctc_greed_decoder)...

API/OP (center_loss, fluid.one_hot, prroi_pool, roi_pool, ctc_greed_decoder) error message enhancement (#23794)

* error message enchanced, test=develop

* error message enchanced for APIs, test=develop

* error message enhanced for roi_pool, test=develop

* update added code, test=develop

* update fluid/input.py, test=develop

* update fluid/input.py ,test=develop

* fix code style, test=develop

* fix lower converage, test=develop

* error message enhanced for roi pool, test=develop

paddle/fluid/operators/center_loss_op.cc

@@ -27,29 +27,18 @@ class CenterLossOp : public framework::OperatorWithKernel {
       : OperatorWithKernel(type, inputs, outputs, attrs) {}
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of CenterLoss should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CenterLoss");
     auto x_dims = ctx->GetInputDim("X");
 
-    PADDLE_ENFORCE(ctx->HasInput("CenterUpdateRate"),
-                   "Input(CenterUpdateRate) of CenterLoss should not be null.");
-
-    PADDLE_ENFORCE(ctx->HasInput("Label"),
-                   "Input(Label) of CenterLoss should not be null.");
-
-    PADDLE_ENFORCE(ctx->HasInput("Centers"),
-                   "Input(Centers) of CenterLoss should not be null.");
-
-    PADDLE_ENFORCE(
-        ctx->HasOutput("SampleCenterDiff"),
-        "Output(SampleCenterDiff) of CenterLoss should not be null.");
-
-    PADDLE_ENFORCE(ctx->HasOutput("Loss"),
-                   "Output(Loss) of CenterLoss should not be null.");
-
-    PADDLE_ENFORCE(
-        ctx->HasOutput("CentersOut"),
-        "Output(CentersOut) of CenterLoss shared data with Centers.");
+    OP_INOUT_CHECK(ctx->HasInput("CenterUpdateRate"), "Input",
+                   "CenterUpdateRate", "CenterLoss");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "CenterLoss");
+    OP_INOUT_CHECK(ctx->HasInput("Centers"), "Input", "Centers", "CenterLoss");
+    OP_INOUT_CHECK(ctx->HasOutput("SampleCenterDiff"), "Output",
+                   "SampleCenterDiff", "CenterLoss");
+    OP_INOUT_CHECK(ctx->HasOutput("Loss"), "Output", "Loss", "CenterLoss");
+    OP_INOUT_CHECK(ctx->HasOutput("CentersOut"), "Output", "CentersOut",
+                   "CenterLoss");
 
     ctx->SetOutputDim("SampleCenterDiff",
                       {x_dims[0], product(x_dims) / x_dims[0]});
@@ -99,12 +88,12 @@ class CenterLossGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("SampleCenterDiff"),
-                   "Input(SampleCenterDiff) should not be null");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
-                   "Input(Loss) should not be null");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
-                   "Output(X) should not be null");
+    OP_INOUT_CHECK(ctx->HasInput("SampleCenterDiff"), "Input",
+                   "SampleCenterDiff", "CenterLossGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Loss")), "Input",
+                   framework::GradVarName("Loss"), "CenterLossGrad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "CenterLossGrad");
 
     auto x_dims = ctx->GetInputDim("X");
     auto x_grad_name = framework::GradVarName("X");

paddle/fluid/operators/ctc_align_op.cc

@@ -22,10 +22,8 @@ class CTCAlignOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
-                      "Input of CTCAlignOp should not be null.");
-    PADDLE_ENFORCE_EQ(ctx->HasOutput("Output"), true,
-                      "Output of CTCAlignOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input", "ctc_align");
+    OP_INOUT_CHECK(ctx->HasOutput("Output"), "Output", "Output", "ctc_align");
 
     auto input_dims = ctx->GetInputDim("Input");
 

paddle/fluid/operators/one_hot_v2_op.cc

@@ -24,14 +24,13 @@ class OneHotV2Op : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
-                      "Input(X) of OneHotOp should not be null.");
-    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
-                      "Output(Out) of OneHotOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "one_hot_v2");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "one_hot_v2");
 
     auto x_dims = ctx->GetInputDim("X");
     PADDLE_ENFORCE_GE(x_dims.size(), 1,
-                      "Rank of Input(X) should be at least 1.");
+                      platform::errors::InvalidArgument(
+                          "Rank of Input(X) should be at least 1."));
 
     int depth = ctx->Attrs().Get<int>("depth");
     if (ctx->HasInput("depth_tensor")) {

paddle/fluid/operators/prroi_pool_op.cc

@@ -79,15 +79,10 @@ class PRROIPoolOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
-                      platform::errors::NotFound(
-                          "Input(X) of op(PRROIPool) should not be null."));
-    PADDLE_ENFORCE_EQ(ctx->HasInput("ROIs"), true,
-                      platform::errors::NotFound(
-                          "Input(ROIs) of op(PRROIPool) should not be null."));
-    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
-                      platform::errors::NotFound(
-                          "Output(Out) of op(PRROIPool) should not be null."));
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "prroi_pool");
+    OP_INOUT_CHECK(ctx->HasInput("ROIs"), "Input", "ROIs", "prroi_pool");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Input", "Out", "prroi_pool");
+
     auto input_dims = ctx->GetInputDim("X");
     auto rois_dims = ctx->GetInputDim("ROIs");
 
@@ -148,10 +143,10 @@ class PRROIPoolGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
-                      "The gradient of Out should not be null.");
-    PADDLE_ENFORCE_EQ(ctx->HasOutput(framework::GradVarName("X")), true,
-                      "The gradient of X should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "prroi_pool");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "prroi_pool");
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
     ctx->SetOutputDim(framework::GradVarName("ROIs"), ctx->GetInputDim("ROIs"));
   }

paddle/fluid/operators/roi_pool_op.cc

@@ -26,39 +26,58 @@ class ROIPoolOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of ROIPoolOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("ROIs"),
-                   "Input(ROIs) of ROIPoolOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of ROIPoolOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Argmax"),
-                   "Output(Argmax) of ROIPoolOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "roi_pool");
+    OP_INOUT_CHECK(ctx->HasInput("ROIs"), "Input", "ROIs", "roi_pool");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "roi_pool");
+    OP_INOUT_CHECK(ctx->HasOutput("Argmax"), "Output", "Argmax", "roi_pool");
+
     auto input_dims = ctx->GetInputDim("X");
     auto rois_dims = ctx->GetInputDim("ROIs");
+
     if (ctx->HasInput("RoisLod")) {
       auto rois_lod_dims = ctx->GetInputDim("RoisLod");
       PADDLE_ENFORCE(rois_lod_dims.size() == 1, "");
     }
-    PADDLE_ENFORCE(input_dims.size() == 4,
-                   "The format of input tensor is NCHW.");
-    PADDLE_ENFORCE(rois_dims.size() == 2,
-                   "ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
-                   "given as [[x1, y1, x2, y2], ...].");
-    PADDLE_ENFORCE(rois_dims[1] == kROISize,
-                   "ROIs should be a 2-D LoDTensor of shape (num_rois, 4)"
-                   "given as [[x1, y1, x2, y2], ...].");
+    PADDLE_ENFORCE_EQ(input_dims.size(), 4,
+                      platform::errors::InvalidArgument(
+                          "The input data should be a four-dimensional "
+                          "tensor with [N,C,H,W], but received input data with "
+                          " %d dimension",
+                          input_dims.size()));
+    PADDLE_ENFORCE_EQ(
+        rois_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "ROIs should be a 2-D LoDTensor with shape (num_rois, 4)"
+            "given as [[x1, y1, x2, y2], ...], but received ROIs is "
+            "%d-dimensional LoDTensor",
+            rois_dims.size()));
+    PADDLE_ENFORCE_EQ(
+        rois_dims[1], kROISize,
+        platform::errors::InvalidArgument(
+            "ROIs should be a 2-D LoDTensor with shape (num_rois, 4)"
+            "given as [[x1, y1, x2, y2], ...]. But the second dimension of  "
+            "the received data is %d",
+            rois_dims[1]));
 
     int pooled_height = ctx->Attrs().Get<int>("pooled_height");
     int pooled_width = ctx->Attrs().Get<int>("pooled_width");
     float spatial_scale = ctx->Attrs().Get<float>("spatial_scale");
 
     PADDLE_ENFORCE_GT(pooled_height, 0,
-                      "The pooled output height must greater than 0");
+                      platform::errors::OutOfRange(
+                          "The pooled output height must be greater than 0"
+                          "but received height is %d",
+                          pooled_height));
     PADDLE_ENFORCE_GT(pooled_width, 0,
-                      "The pooled output width must greater than 0");
+                      platform::errors::OutOfRange(
+                          "The pooled output width must be greater than 0"
+                          "but received width is %d",
+                          pooled_width));
     PADDLE_ENFORCE_GT(spatial_scale, 0.0f,
-                      "The spatial scale must greater than 0");
+                      platform::errors::OutOfRange(
+                          "The spatial scale must be greater than 0, "
+                          "but received spatial scale is %f",
+                          spatial_scale));
 
     auto out_dims = input_dims;
     out_dims[0] = rois_dims[0];
@@ -84,10 +103,10 @@ class ROIPoolGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "The gradient of Out should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
-                   "The gradient of X should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "roi_pool");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "roi_pool");
     ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("X"));
   }
 

python/paddle/fluid/input.py

@@ -98,6 +98,7 @@ def one_hot(input, depth, allow_out_of_range=False):
             label = fluid.data(name="label", shape=[4], dtype="int64")
             one_hot_label = fluid.one_hot(input=label, depth=4)
     """
+    check_variable_and_dtype(input, 'input', ['int32', 'int64'], 'one_hot_v2')
     helper = LayerHelper("one_hot_v2", **locals())
 
     one_hot_out = helper.create_variable_for_type_inference(dtype='float32')

python/paddle/fluid/layers/loss.py

@@ -101,6 +101,10 @@ def center_loss(input,
     """
     helper = LayerHelper('center_loss', **locals())
     dtype = helper.input_dtype()
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                             'center_loss')
+    check_variable_and_dtype(label, 'label', ['int32', 'int64'], 'center_loss')
+
     centers_shape = [num_classes, input.shape[1]]
     centers_param = helper.create_parameter(
         attr=param_attr, shape=centers_shape, dtype=dtype)
@@ -108,6 +112,8 @@ def center_loss(input,
 
     if isinstance(alpha, Variable):
         alpha_param = alpha
+        check_variable_and_dtype(alpha, 'alpha', ['float32', 'float64'],
+                                 'center_loss')
     else:
         assert isinstance(alpha, float)
         alpha_param = helper.create_variable(

python/paddle/fluid/layers/nn.py

@@ -5235,6 +5235,9 @@ def ctc_greedy_decoder(input,
                             input_length=x_pad_len)
 
     """
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                             'ctc_greedy_decoder')
+
     helper = LayerHelper("ctc_greedy_decoder", **locals())
     _, topk_indices = topk(input, k=1)
 
@@ -6715,6 +6718,8 @@ def roi_pool(input,
         print(out)   #array([[[[11.]]], [[[16.]]]], dtype=float32)
         print(np.array(out).shape)  # (2, 1, 1, 1)
     """
+    check_variable_and_dtype(input, 'input', ['float32'], 'roi_pool')
+    check_variable_and_dtype(rois, 'rois', ['float32'], 'roi_pool')
     helper = LayerHelper('roi_pool', **locals())
     dtype = helper.input_dtype()
     pool_out = helper.create_variable_for_type_inference(dtype)
@@ -13096,6 +13101,8 @@ def prroi_pool(input,
 
 
     """
+    check_variable_and_dtype(input, 'input', ['float32'], 'prroi_pool')
+    check_variable_and_dtype(rois, 'rois', ['float32'], 'prroi_pool')
     helper = LayerHelper('prroi_pool', **locals())
     # check attrs
     if not isinstance(spatial_scale, float):

python/paddle/fluid/tests/unittests/test_center_loss.py

@@ -18,6 +18,7 @@ import unittest
 import numpy as np
 from op_test import OpTest
 import paddle.fluid.core as core
+import paddle.fluid as fluid
 
 
 class TestCenterLossOp(OpTest):
@@ -91,5 +92,64 @@ class TestCenterLossOpNoUpdate(TestCenterLossOp):
         self.need_update = False
 
 
+class BadInputTestCenterLoss(unittest.TestCase):
+    def test_error(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_bad_x():
+                data = [[1, 2, 3, 4], [5, 6, 7, 8]]
+                label = fluid.layers.data(
+                    name='label', shape=[2, 1], dtype='int32')
+                res = fluid.layers.center_loss(
+                    data,
+                    label,
+                    num_classes=1000,
+                    alpha=0.2,
+                    param_attr=fluid.initializer.Xavier(uniform=False),
+                    update_center=True)
+
+            self.assertRaises(TypeError, test_bad_x)
+
+            def test_bad_y():
+                data = fluid.layers.data(
+                    name='data', shape=[2, 32], dtype='float32')
+                label = [[2], [3]]
+                res = fluid.layers.center_loss(
+                    data,
+                    label,
+                    num_classes=1000,
+                    alpha=0.2,
+                    param_attr=fluid.initializer.Xavier(uniform=False),
+                    update_center=True)
+
+            self.assertRaises(TypeError, test_bad_y)
+
+            def test_bad_alpha():
+                data = fluid.layers.data(
+                    name='data2',
+                    shape=[2, 32],
+                    dtype='float32',
+                    append_batch_size=False)
+                label = fluid.layers.data(
+                    name='label2',
+                    shape=[2, 1],
+                    dtype='int32',
+                    append_batch_size=False)
+                alpha = fluid.layers.data(
+                    name='alpha',
+                    shape=[1],
+                    dtype='int64',
+                    append_batch_size=False)
+                res = fluid.layers.center_loss(
+                    data,
+                    label,
+                    num_classes=1000,
+                    alpha=alpha,
+                    param_attr=fluid.initializer.Xavier(uniform=False),
+                    update_center=True)
+
+            self.assertRaises(TypeError, test_bad_alpha)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_ctc_align.py

@@ -217,5 +217,16 @@ class TestCTCAlignOpApi(unittest.TestCase):
                       return_numpy=False)
 
 
+class BadInputTestCTCAlignr(unittest.TestCase):
+    def test_error(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_bad_x():
+                x = fluid.layers.data(name='x', shape=[8], dtype='int64')
+                cost = fluid.layers.ctc_greedy_decoder(input=x, blank=0)
+
+            self.assertRaises(TypeError, test_bad_x)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_one_hot_v2_op.py

@@ -204,5 +204,20 @@ class TestOneHotOpApi(unittest.TestCase):
                       return_numpy=False)
 
 
+class BadInputTestOnehotV2(unittest.TestCase):
+    def test_error(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_bad_x():
+                label = fluid.layers.data(
+                    name="label",
+                    shape=[4],
+                    append_batch_size=False,
+                    dtype="float32")
+                one_hot_label = fluid.one_hot(input=label, depth=4)
+
+            self.assertRaises(TypeError, test_bad_x)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_prroi_pool_op.py

@@ -251,6 +251,33 @@ class TestPRROIPoolOpTensorRoIs(OpTest):
             self.assertRaises(TypeError, fluid.layers.prroi_pool, x, rois, 0.25,
                               7, 0.7)
 
+            def test_bad_x():
+                x = fluid.layers.data(
+                    name='data1',
+                    shape=[2, 3, 16, 16],
+                    dtype='int64',
+                    append_batch_size=False)
+                label = fluid.layers.data(
+                    name='label1',
+                    shape=[2, 4],
+                    dtype='float32',
+                    lod_level=1,
+                    append_batch_size=False)
+                output = fluid.layers.prroi_pool(x, label, 0.25, 2, 2)
+
+            self.assertRaises(TypeError, test_bad_x)
+
+            def test_bad_y():
+                x = fluid.layers.data(
+                    name='data2',
+                    shape=[2, 3, 16, 16],
+                    dtype='float32',
+                    append_batch_size=False)
+                label = [[1, 2, 3, 4], [2, 3, 4, 5]]
+                output = fluid.layers.prroi_pool(x, label, 0.25, 2, 2)
+
+            self.assertRaises(TypeError, test_bad_y)
+
 
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_roi_pool_op.py

@@ -20,6 +20,7 @@ import math
 import sys
 import paddle.compat as cpt
 from op_test import OpTest
+import paddle.fluid as fluid
 
 
 class TestROIPoolOp(OpTest):
@@ -115,15 +116,15 @@ class TestROIPoolOp(OpTest):
         for bno in range(self.batch_size):
             self.rois_lod[0].append(bno + 1)
             for i in range(bno + 1):
-                x1 = np.random.random_integers(
+                x1 = np.random.randint(
                     0, self.width // self.spatial_scale - self.pooled_width)
-                y1 = np.random.random_integers(
+                y1 = np.random.randint(
                     0, self.height // self.spatial_scale - self.pooled_height)
 
-                x2 = np.random.random_integers(x1 + self.pooled_width,
-                                               self.width // self.spatial_scale)
-                y2 = np.random.random_integers(
-                    y1 + self.pooled_height, self.height // self.spatial_scale)
+                x2 = np.random.randint(x1 + self.pooled_width,
+                                       self.width // self.spatial_scale)
+                y2 = np.random.randint(y1 + self.pooled_height,
+                                       self.height // self.spatial_scale)
 
                 roi = [bno, x1, y1, x2, y2]
                 rois.append(roi)
@@ -141,6 +142,31 @@ class TestROIPoolOp(OpTest):
         self.check_grad(['X'], 'Out')
 
 
+class BadInputTestRoiPool(unittest.TestCase):
+    def test_error(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_bad_x():
+                x = fluid.layers.data(
+                    name='data1', shape=[2, 1, 4, 4], dtype='int64')
+                label = fluid.layers.data(
+                    name='label', shape=[2, 4], dtype='float32', lod_level=1)
+                output = fluid.layers.roi_pool(x, label, 1, 1, 1.0)
+
+            self.assertRaises(TypeError, test_bad_x)
+
+            def test_bad_y():
+                x = fluid.layers.data(
+                    name='data2',
+                    shape=[2, 1, 4, 4],
+                    dtype='float32',
+                    append_batch_size=False)
+                label = [[1, 2, 3, 4], [2, 3, 4, 5]]
+                output = fluid.layers.roi_pool(x, label, 1, 1, 1.0)
+
+            self.assertRaises(TypeError, test_bad_y)
+
+
 class TestROIPoolInLodOp(TestROIPoolOp):
     def set_data(self):
         self.init_test_case()