API/OP (affine_channel, group_norm, layer_norm, random_crop, unpool, … (#24118)

* API/OP (affine_channel, group_norm, layer_norm, random_crop, unpool, log_loss) error message enhancement test=develop

paddle/fluid/operators/affine_channel_op.cc

@@ -61,14 +61,10 @@ class AffineChannelOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of AffineChannelOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Scale"),
-                   "Input(Scale) of AffineChannelOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Bias"),
-                   "Input(Bias) of AffineChannelOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of AffineChannelOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "AffineChannel");
+    OP_INOUT_CHECK(ctx->HasInput("Scale"), "Input", "Scale", "AffineChannel");
+    OP_INOUT_CHECK(ctx->HasInput("Bias"), "Input", "Bias", "AffineChannel");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "AffineChannel");
 
     auto x_dims = ctx->GetInputDim("X");
     auto scale_dims = ctx->GetInputDim("Scale");
@@ -80,13 +76,32 @@ class AffineChannelOp : public framework::OperatorWithKernel {
                            ? x_dims[1]
                            : x_dims[x_dims.size() - 1]);
 
-    PADDLE_ENFORCE_EQ(scale_dims.size(), 1UL);
-    PADDLE_ENFORCE_EQ(b_dims.size(), 1UL);
+    PADDLE_ENFORCE_EQ(
+        scale_dims.size(), 1UL,
+        platform::errors::InvalidArgument(
+            "The dimensions of Input(Scale) must be 1,"
+            "But received the dimensions of Input(Scale) is [%d] ",
+            scale_dims.size()));
+    PADDLE_ENFORCE_EQ(b_dims.size(), 1UL,
+                      platform::errors::InvalidArgument(
+                          "The dimensions of Input(Bias) must be 1,"
+                          "But received the dimensions of Input(Bias) is [%d] ",
+                          scale_dims.size()));
     if (ctx->IsRuntime() || scale_dims[0] > 0) {
-      PADDLE_ENFORCE_EQ(scale_dims[0], C);
+      PADDLE_ENFORCE_EQ(
+          scale_dims[0], C,
+          platform::errors::InvalidArgument(
+              "The first dimension value of Input(Scale) must be [%d],"
+              "But received [%d].",
+              C, scale_dims[0]));
     }
     if (ctx->IsRuntime() || b_dims[0] > 0) {
-      PADDLE_ENFORCE_EQ(b_dims[0], C);
+      PADDLE_ENFORCE_EQ(
+          b_dims[0], C,
+          platform::errors::InvalidArgument(
+              "The first dimension value of Input(Bias) must be [%d],"
+              "But received [%d].",
+              C, b_dims[0]));
     }
 
     ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
@@ -98,19 +113,19 @@ class AffineChannelOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@GRAD) should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
+                   framework::GradVarName("Out"), "AffineChannelGrad");
     if (ctx->HasOutput(framework::GradVarName("X"))) {
-      PADDLE_ENFORCE(ctx->HasInput("Scale"),
-                     "Input(Scale) should not be null.");
+      OP_INOUT_CHECK(ctx->HasInput("Scale"), "Input", "Scale",
+                     "AffineChannelGrad");
       ctx->SetOutputDim(framework::GradVarName("X"),
                         ctx->GetInputDim(framework::GradVarName("Out")));
     }
     if (ctx->HasOutput(framework::GradVarName("Scale"))) {
       // Scale@GRAD and Bias@GRAD must exist at the same time.
-      PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Bias")),
-                     "Output(Scale@GRAD) should not be null.");
-      PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null.");
+      OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("Bias")), "Output",
+                     framework::GradVarName("Bias"), "AffineChannelGrad");
+      OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "AffineChannelGrad");
       ctx->SetOutputDim(framework::GradVarName("Scale"),
                         ctx->GetInputDim("Scale"));
       ctx->SetOutputDim(framework::GradVarName("Bias"),

paddle/fluid/operators/layer_norm_op.cc

@@ -27,36 +27,62 @@ class LayerNormOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of LayerNormOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Y"),
-                   "Output(Y) of LayerNormOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Mean"),
-                   "Output(Mean) of LayerNormOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Variance"),
-                   "Output(Variance) of LayerNormOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "LayerNorm");
+    OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "LayerNorm");
+    OP_INOUT_CHECK(ctx->HasOutput("Mean"), "Output", "Mean", "LayerNorm");
+    OP_INOUT_CHECK(ctx->HasOutput("Variance"), "Output", "Variance",
+                   "LayerNorm");
 
     auto x_dim = ctx->GetInputDim("X");
     auto begin_norm_axis = ctx->Attrs().Get<int>("begin_norm_axis");
-    PADDLE_ENFORCE_LT(begin_norm_axis, x_dim.size(),
-                      "'begin_norm_axis' must be less than the rank of X.");
+    PADDLE_ENFORCE_LT(
+        begin_norm_axis, x_dim.size(),
+        platform::errors::InvalidArgument(
+            "'begin_norm_axis' must be less than the dimensions of X,"
+            "But received 'begin_norm_axis' is [%d],"
+            "received the dimensions of X is [%d].",
+            begin_norm_axis, x_dim.size()));
 
     auto matrix_dim = framework::flatten_to_2d(x_dim, begin_norm_axis);
     int left = static_cast<int>(matrix_dim[0]);
     int right = static_cast<int>(matrix_dim[1]);
     if (ctx->HasInput("Scale")) {
-      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1);
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale").size(), 1,
+                        platform::errors::InvalidArgument(
+                            "The dimensions of Input(Scale) must be 1, but "
+                            "received dimensions of"
+                            "Input(Scale) is [%d]",
+                            ctx->GetInputDim("Scale").size()));
 
       if (ctx->IsRuntime()) {
-        PADDLE_ENFORCE_EQ(ctx->GetInputDim("Scale")[0], right,
-                          "scale should with right");
+        PADDLE_ENFORCE_EQ(
+            ctx->GetInputDim("Scale")[0], right,
+            platform::errors::InvalidArgument(
+                "The first dimension value of Input(Scale) must equal to be the"
+                "second dimension value of the flattened 2D matrix of Input(X),"
+                "But received the first dimension value of Input(Scale) is"
+                "[%d], the second dimension value of the flattened 2D matrix of"
+                " Input(Scale) is [%d].",
+                ctx->GetInputDim("Scale")[0], right));
       }
     }
     if (ctx->HasInput("Bias")) {
-      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1);
+      PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias").size(), 1,
+                        platform::errors::InvalidArgument(
+                            "The dimensions of Input(Bias) must be 1, but "
+                            "received dimensions of"
+                            "Input(Bias) is [%d]",
+                            ctx->GetInputDim("Bias").size()));
       if (ctx->IsRuntime()) {
-        PADDLE_ENFORCE_EQ(ctx->GetInputDim("Bias")[0], right,
-                          "bias should with right");
+        PADDLE_ENFORCE_EQ(
+            ctx->GetInputDim("Bias")[0], right,
+            platform::errors::InvalidArgument(
+                "The first dimension value of Input(Bias) must equal to be the"
+                "second dimension value of the flattened 2D matrix of Input(X),"
+                "But received the first dimension value of Input(Bias) is"
+                "[%d], the second dimension value of the flattened 2D matrix of"
+                " Input(Bias) is [%d].",
+                ctx->GetInputDim("Scale")[0], right));
       }
     }
 
@@ -90,8 +116,11 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
                    "Constant for numerical stability [default 1e-5].")
         .SetDefault(1e-5)
         .AddCustomChecker([](const float &epsilon) {
-          PADDLE_ENFORCE(epsilon >= 0.0f && epsilon <= 0.001f,
-                         "'epsilon' should be between 0.0 and 0.001.");
+          PADDLE_ENFORCE_EQ(epsilon >= 0.0f && epsilon <= 0.001f, true,
+                            platform::errors::InvalidArgument(
+                                "'epsilon' in Op(LayerNorm) should be between"
+                                "0.0 and 0.001, But received [%s].",
+                                epsilon));
         });
     AddAttr<int>("begin_norm_axis",
                  "the axis of `begin_norm_axis ... Rank(X) - 1` will be "
@@ -100,7 +129,10 @@ class LayerNormOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(1)
         .AddCustomChecker([](const int &begin_norm_axis) {
           PADDLE_ENFORCE_GT(begin_norm_axis, 0,
-                            "'begin_norm_axis' should be greater than zero.");
+                            platform::errors::InvalidArgument(
+                                "'begin_norm_axis' in Op(LayerNorm) should be"
+                                "greater than zero. But received [%d].",
+                                begin_norm_axis));
         });
 
     AddComment(R"DOC(
@@ -122,14 +154,12 @@ class LayerNormGradOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     // check input
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of LayerNormOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Mean"),
-                   "Input(Mean) of LayerNormOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Variance"),
-                   "Input(Variance) of LayerNormOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
-                   "Input(Y@GRAD) of LayerNormOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "LayerNormGrad");
+    OP_INOUT_CHECK(ctx->HasInput("Mean"), "Input", "Mean", "LayerNormGrad");
+    OP_INOUT_CHECK(ctx->HasInput("Variance"), "Input", "Variance",
+                   "LayerNormGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), "Input",
+                   framework::GradVarName("Y"), "LayerNormGrad");
 
     // check output
     if (ctx->HasOutput(framework::GradVarName("X"))) {

paddle/fluid/operators/log_loss_op.cc

@@ -23,25 +23,37 @@ class LogLossOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Predicted"),
-                   "Input(Predicted) must be initialized.");
-    PADDLE_ENFORCE(ctx->HasInput("Labels"),
-                   "Input(Labels) must be initialized.");
+    OP_INOUT_CHECK(ctx->HasInput("Predicted"), "Input", "Predicted", "LogLoss");
+    OP_INOUT_CHECK(ctx->HasInput("Labels"), "Input", "Labels", "LogLoss");
 
     auto pred_dims = ctx->GetInputDim("Predicted");
     auto label_dims = ctx->GetInputDim("Labels");
 
     if (ctx->IsRuntime() || (framework::product(pred_dims) > 0 &&
                              framework::product(label_dims) > 0)) {
-      PADDLE_ENFORCE_EQ(pred_dims, label_dims);
+      PADDLE_ENFORCE_EQ(
+          pred_dims, label_dims,
+          platform::errors::InvalidArgument(
+              "The dimensions of Input(Predicted) must be equal to the"
+              "dimensions of Input(Labels), but received dimensions of "
+              "Input(Predicted)"
+              "is [%s], received dimensions of Input(Labels) is [%s].",
+              pred_dims, label_dims));
     }
     PADDLE_ENFORCE_EQ(pred_dims.size(), 2,
-                      "The rank of Input(Predicted) must be 2 and the shape is "
-                      "[batch_size, 1].");
+                      platform::errors::InvalidArgument(
+                          "The dimensions of Input(Predicted) must be 2,"
+                          "But received dimensions of Input(Predicted)"
+                          "is [%d]",
+                          pred_dims.size()));
     if (ctx->IsRuntime()) {
-      PADDLE_ENFORCE_EQ(pred_dims[1], 1,
-                        "Each row of Input(Predicted) contains a real value, "
-                        "so the 2nd dimension of Input(X) must be 1.");
+      PADDLE_ENFORCE_EQ(
+          pred_dims[1], 1,
+          platform::errors::InvalidArgument(
+              "Each row of Input(Predicted) contains a real value, "
+              "so the 2nd dimension of Input(X) must be 1,"
+              "But got [%d]",
+              pred_dims[1]));
     }
     ctx->SetOutputDim("Loss", {pred_dims[0], 1});
     ctx->ShareLoD("Predicted", "Loss");
@@ -87,18 +99,25 @@ class LogLossGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Predicted"),
-                   "Input(Predicted) should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Labels"),
-                   "Input(Labels) should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
-                   "Input(Loss@GRAD) should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("Predicted")),
-                   "Output(Predicted@GRAD) should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("Predicted"), "Input", "Predicted",
+                   "LogLossGrad");
+    OP_INOUT_CHECK(ctx->HasInput("Labels"), "Input", "Labels", "LogLossGrad");
+    OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Loss")), "Input",
+                   framework::GradVarName("Loss"), "LogLossGrad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("Predicted")),
+                   "Output", framework::GradVarName("Predicted"),
+                   "LogLossGrad");
 
     auto pred_dims = ctx->GetInputDim("Predicted");
     auto loss_grad_dims = ctx->GetInputDim(framework::GradVarName("Loss"));
-    PADDLE_ENFORCE_EQ(loss_grad_dims, pred_dims);
+    PADDLE_ENFORCE_EQ(loss_grad_dims, pred_dims,
+                      platform::errors::InvalidArgument(
+                          "The dimensions of loss_grad must be equal to the "
+                          "dimensions of Predicted,"
+                          "But received dimensions of loss_grad is [%s], "
+                          "received Predicted is "
+                          "[%s]",
+                          loss_grad_dims, pred_dims));
 
     auto pred_grad_name = framework::GradVarName("Predicted");
     ctx->SetOutputDim(pred_grad_name, pred_dims);

paddle/fluid/operators/random_crop_op.cc

@@ -27,7 +27,11 @@ class RandomCropOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_GT(
         x_dim.size(), static_cast<int64_t>(shape.size()),
         platform::errors::InvalidArgument(
-            "Rank of Input(X) must be equal to length of Attr(shape)"));
+            "The dimensions of Input(X) must be greater than the length of "
+            "Attr(shape),"
+            "But received dimensions of Input(X) is [%d], receivecd length"
+            "of Attr(shape) is [%d].",
+            x_dim.size(), static_cast<int64_t>(shape.size())));
     auto out_dim = framework::vectorize<int>(x_dim);
     for (size_t i = 1; i <= shape.size(); ++i) {
       size_t x_i = x_dim.size() - i;
@@ -36,7 +40,10 @@ class RandomCropOp : public framework::OperatorWithKernel {
         PADDLE_ENFORCE_GE(
             x_dim[x_i], shape[shape_i],
             platform::errors::InvalidArgument(
-                "Size of Input(X) must be larger than Attr(shape)"));
+                "The dimensions of Input(X) must be larger than Attr(shape),"
+                "But received dimensions of Input(X) is [%d], received"
+                "size of Attr(shape) is [%d].",
+                x_dim[x_i], shape[shape_i]));
       }
       out_dim[x_i] = shape[shape_i];
     }

paddle/fluid/operators/unpool_op.cc

@@ -83,15 +83,9 @@ class UnpoolOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(
-        ctx->HasInput("X"), true,
-        platform::errors::NotFound("Input(X) of UnpoolOp is not found."));
-    PADDLE_ENFORCE_EQ(
-        ctx->HasInput("Indices"), true,
-        platform::errors::NotFound("Input(Indices) of UnpoolOp is not found."));
-    PADDLE_ENFORCE_EQ(
-        ctx->HasOutput("Out"), true,
-        platform::errors::NotFound("Output(Out) of UnpoolOp is not found."));
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Unpool");
+    OP_INOUT_CHECK(ctx->HasInput("Indices"), "Input", "Indices", "Unpool");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Unpool");
     auto in_x_dims = ctx->GetInputDim("X");
     auto in_y_dims = ctx->GetInputDim("Indices");
     std::string unpooling_type =
@@ -101,8 +95,8 @@ class UnpoolOp : public framework::OperatorWithKernel {
     std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
     PADDLE_ENFORCE_EQ(in_x_dims.size() == 4, true,
                       platform::errors::InvalidArgument(
-                          "Unpooling intput(X) must be of 4-dimensional, but "
-                          "received X's dimension is %d.",
+                          "Unpooling Intput(X) must be of 4-dimensional, but "
+                          "received Input(X)'s dimension is %d.",
                           in_x_dims.size()));
     PADDLE_ENFORCE_EQ(in_x_dims, in_y_dims);
 
@@ -146,12 +140,9 @@ class UnpoolOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE_EQ(
-        ctx->HasInput("X"), true,
-        platform::errors::NotFound("Input(X) of UnpoolOpGradOp is not found."));
-    PADDLE_ENFORCE_EQ(ctx->HasOutput(framework::GradVarName("X")), true,
-                      platform::errors::NotFound(
-                          "Input(X@GRAD) of UnpoolOpGradOp is not found."));
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "UnpoolGrad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "UnpoolGrad");
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }
 };

python/paddle/fluid/layers/nn.py

@@ -3406,6 +3406,8 @@ def layer_norm(input,
     assert in_dygraph_mode(
     ) is not True, "please use LayerNorm instead of layer_norm in dygraph mode!"
     helper = LayerHelper('layer_norm', **locals())
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                             'layer_norm')
     dtype = helper.input_dtype()
 
     # create intput and parameters
@@ -3510,7 +3512,8 @@ def group_norm(input,
     """
     helper = LayerHelper('group_norm', **locals())
     dtype = helper.input_dtype()
-
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'],
+                             'group_norm')
     # create intput and parameters
     inputs = {'X': input}
     input_shape = input.shape
@@ -8182,6 +8185,10 @@ def random_crop(x, shape, seed=None):
 
     """
     helper = LayerHelper("random_crop", **locals())
+    check_variable_and_dtype(x, 'x',
+                             ['float32', 'float64', 'uint8', 'int16', 'int32'],
+                             'random_crop')
+    check_type(shape, 'shape', (list, Variable), 'random_crop')
     dtype = x.dtype
     out = helper.create_variable_for_type_inference(dtype)
     if seed is None:
@@ -12072,6 +12079,9 @@ def affine_channel(x,
 
     """
     helper = LayerHelper("affine_channel", **locals())
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'affine_channel')
+    check_type(scale, 'scale', (Variable, type(None)), 'affine_channel')
+    check_type(bias, 'bias', (Variable, type(None)), 'affine_channel')
     out = helper.create_variable_for_type_inference(dtype=x.dtype)
 
     helper.append_op(
@@ -12390,6 +12400,8 @@ def log_loss(input, label, epsilon=1e-4, name=None):
           cost = fluid.layers.log_loss(input=prob, label=label)
     """
     helper = LayerHelper('log_loss', **locals())
+    check_variable_and_dtype(input, 'input', ['float32'], 'log_loss')
+    check_variable_and_dtype(label, 'label', ['float32'], 'log_loss')
 
     loss = helper.create_variable_for_type_inference(dtype=input.dtype)
 

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

@@ -21,6 +21,7 @@ import unittest
 import numpy as np
 from op_test import OpTest
 import paddle.fluid.core as core
+import paddle.fluid as fluid
 
 
 def affine_channel(x, scale, bias, layout):
@@ -67,6 +68,38 @@ class TestAffineChannelOp(OpTest):
         self.layout = 'NCHW'
 
 
+class TestAffineChannelOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_x_type():
+                input_data = np.random.random(2, 1, 2, 2).astype("float32")
+                fluid.layers.affine_channel(input_data)
+
+            self.assertRaises(TypeError, test_x_type)
+
+            def test_x_dtype():
+                x2 = fluid.layers.data(
+                    name='x2', shape=[None, 1, 2, 2], dtype='int32')
+                fluid.layers.affine_channel(x2)
+
+            self.assertRaises(TypeError, test_x_dtype)
+
+            def test_scale_type():
+                x3 = fluid.layers.data(
+                    name='x3', shape=[None, 1, 2, 2], dtype='float32')
+                fluid.layers.affine_channel(x3, scale=1)
+
+            self.assertRaises(TypeError, test_scale_type)
+
+            def test_bias_type():
+                x4 = fluid.layers.data(
+                    name='x4', shape=[None, 1, 2, 2], dtype='float32')
+                fluid.layers.affine_channel(x4, bias=1)
+
+            self.assertRaises(TypeError, test_bias_type)
+
+
 class TestAffineChannelNHWC(TestAffineChannelOp):
     def init_test_case(self):
         self.shape = [2, 12, 12, 100]

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

@@ -40,6 +40,26 @@ def group_norm_naive(x, scale, bias, epsilon, groups, data_layout):
     return output, mean.reshape((N, G)), var.reshape((N, G))
 
 
+class TestGroupNormOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+
+            def test_x_type():
+                input = np.random.random(2, 100, 3, 5).astype('float32')
+                goups = 2
+                fluid.layers.group_norm(input, groups)
+
+            self.assertRaises(TypeError, test_x_type)
+
+            def test_x_dtype():
+                x2 = fluid.layers.data(
+                    name='x2', shape=[2, 100, 3, 5], dtype='int32')
+                groups = 2
+                fluid.layers.group_norm(x2, groups)
+
+            self.assertRaises(TypeError, test_x_dtype)
+
+
 class TestGroupNormOp(OpTest):
     def setUp(self):
         self.op_type = "group_norm"

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

@@ -17,6 +17,7 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid as fluid
 
 
 def sigmoid_array(x):
@@ -49,5 +50,34 @@ class TestLogLossOp(OpTest):
         self.check_grad(['Predicted'], 'Loss', max_relative_error=0.03)
 
 
+class TestLogLossOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_x_type():
+                input_data = np.random.random(100, 1).astype("float32")
+                fluid.layers.log_loss(input_data)
+
+            self.assertRaises(TypeError, test_x_type)
+
+            def test_x_dtype():
+                x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
+                fluid.layers.log_loss(x2)
+
+            self.assertRaises(TypeError, test_x_dtype)
+
+            def test_label_type():
+                input_data = np.random.random(100, 1).astype("float32")
+                fluid.layers.log_loss(input_data)
+
+            self.assertRaises(TypeError, test_label_type)
+
+            def test_label_dtype():
+                x2 = fluid.layers.data(name='x2', shape=[100, 1], dtype='int32')
+                fluid.layers.log_loss(x2)
+
+            self.assertRaises(TypeError, test_label_dtype)
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -18,6 +18,7 @@ import unittest
 import numpy as np
 import paddle.fluid.core as core
 from op_test import OpTest
+import paddle.fluid as fluid
 
 
 class TestRandomCropOp(OpTest):
@@ -45,5 +46,30 @@ class TestRandomCropOp(OpTest):
             self.assertIn(True, is_equal)
 
 
+class TestRandomCropOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program()):
+
+            def test_x_type():
+                input_data = np.random.random(2, 3, 256, 256).astype("float32")
+                fluid.layers.random_crop(input_data)
+
+            self.assertRaises(TypeError, test_x_type)
+
+            def test_x_dtype():
+                x2 = fluid.layers.data(
+                    name='x2', shape=[None, 3, 256, 256], dtype='float16')
+                fluid.layers.random_crop(x2)
+
+            self.assertRaises(TypeError, test_x_dtype)
+
+            def test_shape_type():
+                x3 = fluid.layers.data(
+                    name='x3', shape=[None, 3, 256, 256], dtype='float32')
+                fluid.layers.random_crop(x3, shape=1)
+
+            self.assertRaises(TypeError, test_shape_type)
+
+
 if __name__ == "__main__":
     unittest.main()