All elements in attr(shape) of crop_tensor can be -1 and int32/64 kernel registered (#20756)

* All elements in attr(shape) of crop_tensor can be -1, test=develop, test=document_preview

* fix the bug that attr(offsets) should be initialized, test=develop

paddle/fluid/operators/crop_tensor_op.cc

@@ -31,8 +31,9 @@ class CropTensorOp : public framework::OperatorWithKernel {
                       "Input(X) of Op(crop_tensor) should not be null.");
     PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
                       "Output(Out) of Op(crop_tensor) should not be null.");
-
+    auto x_dim = ctx->GetInputDim("X");
     auto shape = ctx->Attrs().Get<std::vector<int>>("shape");
+    auto offsets = ctx->Attrs().Get<std::vector<int>>("offsets");
     if (ctx->HasInputs("ShapeTensor")) {
       // top prority shape
       auto inputs_name = ctx->Inputs("ShapeTensor");
@@ -43,15 +44,19 @@ class CropTensorOp : public framework::OperatorWithKernel {
           "Op(fluid.layers.crop_tensor).");
       auto out_dims = std::vector<int>(inputs_name.size(), -1);
       for (size_t i = 0; i < shape.size(); ++i) {
-        if (shape[i] != -1) {
+        if (shape[i] > 0) {
           out_dims[i] = static_cast<int64_t>(shape[i]);
+        } else {
+          if (shape[i] == -1 && offsets[i] != -1 && x_dim[i] != -1) {
+            out_dims[i] = x_dim[i] - static_cast<int64_t>(offsets[i]);
+          }
         }
       }
       ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
 
       return;
     }
-    auto x_dim = ctx->GetInputDim("X");
+
     if (ctx->HasInput("Shape")) {
       auto shape_dim = ctx->GetInputDim("Shape");
       PADDLE_ENFORCE_EQ(
@@ -78,11 +83,17 @@ class CropTensorOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(int64_t(shape.size()), x_dim.size(),
                       "Attr(shape)'size of Op(crop_tensor) should be equal to "
                       "dimention size of input tensor.");
-    std::vector<int64_t> tensor_shape(shape.size());
+    std::vector<int64_t> out_shape(shape.size(), -1);
     for (size_t i = 0; i < shape.size(); ++i) {
-      tensor_shape[i] = static_cast<int64_t>(shape[i]);
+      if (shape[i] > 0) {
+        out_shape[i] = static_cast<int64_t>(shape[i]);
+      } else {
+        if (shape[i] == -1 && offsets[i] != -1 && x_dim[i] != -1) {
+          out_shape[i] = x_dim[i] - static_cast<int64_t>(offsets[i]);
+        }
+      }
     }
-    ctx->SetOutputDim("Out", framework::make_ddim(tensor_shape));
+    ctx->SetOutputDim("Out", framework::make_ddim(out_shape));
   }
 
   framework::OpKernelType GetExpectedKernelType(
@@ -293,8 +304,12 @@ REGISTER_OPERATOR(crop_tensor_grad, ops::CropTensorOpGrad);
 REGISTER_OP_CPU_KERNEL(
     crop_tensor,
     ops::CropTensorKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::CropTensorKernel<paddle::platform::CPUDeviceContext, double>);
+    ops::CropTensorKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::CropTensorKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::CropTensorKernel<paddle::platform::CPUDeviceContext, int64_t>);
 REGISTER_OP_CPU_KERNEL(
     crop_tensor_grad,
     ops::CropTensorGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::CropTensorGradKernel<paddle::platform::CPUDeviceContext, double>);
+    ops::CropTensorGradKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::CropTensorGradKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::CropTensorGradKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/crop_tensor_op.cu

@@ -17,8 +17,12 @@ namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
     crop_tensor,
     ops::CropTensorKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::CropTensorKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::CropTensorKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::CropTensorKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::CropTensorKernel<paddle::platform::CUDADeviceContext, int64_t>);
 REGISTER_OP_CUDA_KERNEL(
     crop_tensor_grad,
     ops::CropTensorGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::CropTensorGradKernel<paddle::platform::CUDADeviceContext, double>);
+    ops::CropTensorGradKernel<paddle::platform::CUDADeviceContext, double>,
+    ops::CropTensorGradKernel<paddle::platform::CUDADeviceContext, int>,
+    ops::CropTensorGradKernel<paddle::platform::CUDADeviceContext, int64_t>);

paddle/fluid/operators/crop_tensor_op.h

@@ -50,29 +50,28 @@ inline std::vector<int> get_new_data(
 }
 
 static framework::DDim ValidateShape(const std::vector<int> shape,
+                                     const std::vector<int> offsets,
                                      const framework::DDim& in_dims) {
   auto in_dim_size = in_dims.size();
   auto shape_size = shape.size();
   PADDLE_ENFORCE_EQ(
       in_dim_size, shape_size,
-      "Input(ShapeTensor)'s dimension size of Op(crop_tensor) should be equal "
-      "to that of input tensor. "
+      "Attr(shape)'s size of Op(crop_tensor) should be equal "
+      "to that of input Tensor. "
       "Please check the Attr(shape)'s size of Op(fluid.layers.crop_tensor).");
-  const int64_t unk_dim_val = -1;
-  int unk_dim_idx = -1;
   std::vector<int64_t> output_shape(shape.size(), 0);
   for (size_t i = 0; i < shape.size(); ++i) {
-    if (shape[i] == unk_dim_val) {
-      PADDLE_ENFORCE_EQ(unk_dim_idx, -1,
-                        "Only one element of shape can be unknown.");
-      PADDLE_ENFORCE_EQ(i, 0, "Only the first element of shape can be -1.");
-      unk_dim_idx = i;
+    if (shape[i] <= 0 && in_dims[i] > 0) {
+      PADDLE_ENFORCE_NE(
+          shape[i], 0,
+          "The element in Attr(shape) of Op(crop_tensor) should not be zero.");
+      PADDLE_ENFORCE_EQ(shape[i], -1,
+                        "When the element in Attr(shape) of Op(crop_tensor) is "
+                        "negative, only -1 is supported.");
+      output_shape[i] = in_dims[i] - offsets[i];
     } else {
-      PADDLE_ENFORCE_GT(shape[i], 0,
-                        "Each element of shape must be greater than 0 "
-                        "except the first element.");
+      output_shape[i] = static_cast<int64_t>(shape[i]);
     }
-    output_shape[i] = static_cast<int64_t>(shape[i]);
   }
 
   return framework::make_ddim(output_shape);
@@ -164,21 +163,15 @@ void CropTensorFunction(const framework::ExecutionContext& context) {
       shape.push_back(out_dims[i]);
     }
   }
-  out_dims = ValidateShape(shape, x->dims());
-  if (out_dims[0] == -1) {
-    out_dims[0] = x->dims()[0];
-  }
 
-  out->mutable_data<T>(out_dims, context.GetPlace());
-  auto x_stride = framework::stride(x->dims());
   auto offsets = GetOffsets(context);
-  int64_t offset = 0;
+  out_dims = ValidateShape(shape, offsets, x->dims());
+  out->mutable_data<T>(out_dims, context.GetPlace());
   for (size_t i = 0; i < offsets.size(); ++i) {
     PADDLE_ENFORCE_LE(
         offsets[i] + shape[i], x_dims[i],
         "The sum of the Attr(offsets) and Attr(shape) of Op(crop_tensor) "
         "should be less than or equal to corresponding input dimension size.");
-    offset += (x_stride[i] * offsets[i]);
   }
 
   auto x_tensor = EigenTensor<T, D>::From(*x);

python/paddle/fluid/layers/nn.py

@@ -11391,7 +11391,7 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
 
         * Case 1 (input is a 2-D Tensor):
             Input:
-                X.shape = [3. 5]
+                X.shape = [3, 5]
                 X.data = [[0, 1, 2, 0, 0],
                           [0, 3, 4, 0, 0],
                           [0, 0, 0, 0, 0]]
@@ -11399,8 +11399,9 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
                 shape = [2, 2]
                 offsets = [0, 1]
             Output:
-                Out = [[1, 2],
-                       [3, 4]]
+                Out.shape = [2, 2]
+                Out.data = [[1, 2],
+                            [3, 4]]
         * Case 2 (input is a 3-D Tensor):
             Input:
                 X.shape = [2, 3, 4]
@@ -11411,24 +11412,23 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
                             [0, 6, 7, 8],
                             [0, 0, 0, 0]]]
             Parameters:
-                shape = [2, 2, 3]
+                shape = [2, 2, -1]
                 offsets = [0, 0, 1]
             Output:
-                Out = [[[1, 2, 3],
-                        [5, 6, 7]],
-                       [[3, 4, 5],
-                        [6, 7, 8]]]
+                Out.shape = [2, 2, 3]
+                Out.data  = [[[1, 2, 3],
+                              [5, 6, 7]],
+                             [[3, 4, 5],
+                              [6, 7, 8]]]
 
     Parameters:
-        x (Variable): 1-D to 6-D Tensor, the data type is float32 or float64.
+        x (Variable): 1-D to 6-D Tensor, the data type is float32, float64, int32 or int64.
         shape (list|tuple|Variable): The output shape is specified
             by `shape`. Its data type is int32. If a list/tuple, it's length must be
             the same as the dimension size of `x`. If a Variable, it shoule be a 1-D Tensor.
             When it is a list, each element can be an integer or a Tensor of shape: [1].
-            If Variable contained, it is suitable for the case that the shape may 
-            be changed each iteration. Only the first element of list/tuple can be 
-            set to -1, it means that the first dimension's size of the output is the same 
-            as the input.
+            If Variable contained, it is suitable for the case that the shape may
+            be changed each iteration.
         offsets (list|tuple|Variable, optional): Specifies the cropping
             offsets at each dimension. Its data type is int32. If a list/tuple, it's length
             must be the same as the dimension size of `x`. If a Variable, it shoule be a 1-D
@@ -11442,8 +11442,12 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
         Variable: The cropped Tensor has same data type with `x`.
 
     Raises:
-        ValueError: If shape is not a list, tuple or Variable.
-        ValueError: If offsets is not None and not a list, tuple or Variable.
+        TypeError: If the data type of `x` is not in: float32, float64, int32, int64.
+        TypeError: If `shape` is not a list, tuple or Variable.
+        TypeError: If the data type of `shape` is not int32.
+        TypeError: If `offsets` is not None and not a list, tuple or Variable.
+        TypeError: If the data type of `offsets` is not int32.
+        ValueError: If the element in `offsets` is less than zero.
 
     Examples:
 
@@ -11459,7 +11463,7 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
             # crop0.shape = [-1, -1, -1], it means crop0.shape[0] = x.shape[0] in runtime.
 
             # or shape is a list in which each element is a constant
-            crop1 = fluid.layers.crop_tensor(x, shape=[-1, 2, 3])
+            crop1 = fluid.layers.crop_tensor(x, shape=[-1, -1, 3], offsets=[0, 1, 0])
             # crop1.shape = [-1, 2, 3]
 
             # or shape is a list in which each element is a constant or Variable
@@ -11481,70 +11485,98 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
     """
     helper = LayerHelper('crop_tensor', **locals())
 
+    if convert_dtype(x.dtype) not in ['float32', 'float64', 'int32', 'int64']:
+        raise TypeError(
+            "Input(x)'s dtype of Op(crop_tensor) must be float32, float64, int32 or int64, "
+            "but received %s." % (convert_dtype(x.dtype)))
+
     if not (isinstance(shape, list) or isinstance(shape, tuple) or \
             isinstance(shape, Variable)):
-        raise ValueError("The shape should be a list, tuple or Variable.")
+        raise TypeError(
+            "Attr(shape) of Op(crop_tensor) should be a list, tuple or Variable."
+        )
 
     if offsets is None:
         offsets = [0] * len(x.shape)
 
     if not (isinstance(offsets, list) or isinstance(offsets, tuple) or \
             isinstance(offsets, Variable)):
-        raise ValueError("The offsets should be a list, tuple or Variable.")
+        raise TypeError(
+            "Attr(offsets) of Op(crop_tensor) should be a list, tuple or Variable."
+        )
 
     out = helper.create_variable_for_type_inference(x.dtype)
     ipts = {'X': x}
     attrs = {}
 
-    def contain_var(input_list):
+    def _contain_var(input_list):
         for ele in input_list:
             if isinstance(ele, Variable):
                 return True
         return False
 
+    def _attr_shape_check(shape_val):
+        if not isinstance(shape_val, int):
+            raise TypeError(
+                "Attr(shape)'s dtype of Op(crop_tensor) should be int32, but received: %s."
+                % type(shape_val))
+        if shape_val == 0:
+            raise ValueError(
+                "Attr(shape) of Op(crop_tensor) should not be zero, but received: %s."
+                % str(shape_val))
+        if shape_val < -1:
+            raise ValueError(
+                "When the element in Attr(shape) of Op(crop_tensor) is negative, only -1 is supported, but received: %s."
+                % str(shape_val))
+
+    def _attr_offsets_check(offset_val):
+        if not isinstance(offset_val, int):
+            raise TypeError(
+                "Attr(offsets)'s dtype of Op(crop_tensor) should be int32, but received: %s."
+                % type(offset_val))
+        if offset_val < 0:
+            raise ValueError(
+                "Attr(offsets) of Op(crop_tensor) should be greater or equal to zero, but received: %s."
+                % str(offset_val))
+
     if isinstance(offsets, Variable):
         offsets.stop_gradient = True
         ipts['Offsets'] = offsets
-    elif contain_var(offsets):
+        attrs['offsets'] = [-1] * len(x.shape)
+    elif _contain_var(offsets):
         new_offsets_tensor = []
+        offsets_attr = []
         for dim in offsets:
             if isinstance(dim, Variable):
                 dim.stop_gradient = True
                 new_offsets_tensor.append(dim)
+                offsets_attr.append(-1)
             else:
-                assert (isinstance(dim, int))
-                assert dim >= 0, ("offsets should be greater or equal to zero.")
+                _attr_offsets_check(dim)
                 temp_out = helper.create_variable_for_type_inference('int32')
                 fill_constant([1], 'int32', dim, force_cpu=True, out=temp_out)
                 new_offsets_tensor.append(temp_out)
+                offsets_attr.append(dim)
         ipts['OffsetsTensor'] = new_offsets_tensor
+        attrs['offsets'] = offsets_attr
     else:
+        for offset in offsets:
+            _attr_offsets_check(offset)
         attrs['offsets'] = offsets
 
-    unk_dim_idx = -1
     if isinstance(shape, Variable):
         shape.stop_gradient = True
         ipts['Shape'] = shape
-    elif contain_var(shape):
+    elif _contain_var(shape):
         new_shape_tensor = []
         shape_attr = []
-        for dim_idx, dim_size in enumerate(shape):
+        for dim_size in shape:
             if isinstance(dim_size, Variable):
                 dim_size.stop_gradient = True
                 new_shape_tensor.append(dim_size)
-                shape_attr.append(-1)
+                shape_attr.append(0)
             else:
-                assert (isinstance(dim_size, int))
-                if dim_size == -1:
-                    assert unk_dim_idx == -1, (
-                        "Only one element in shape can be unknown.")
-                    assert dim_idx == 0, (
-                        "Only the first element in shape can be -1.")
-                    unk_dim_idx = dim_idx
-                else:
-                    assert dim_size > 0, (
-                        "Each dimension size given in shape must be greater than zero."
-                    )
+                _attr_shape_check(dim_size)
                 temp_out = helper.create_variable_for_type_inference('int32')
                 fill_constant(
                     [1], 'int32', dim_size, force_cpu=True, out=temp_out)
@@ -11553,6 +11585,8 @@ def crop_tensor(x, shape=None, offsets=None, name=None):
         ipts['ShapeTensor'] = new_shape_tensor
         attrs['shape'] = shape_attr
     else:
+        for dim_size in shape:
+            _attr_shape_check(dim_size)
         attrs['shape'] = shape
 
     helper.append_op(

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

@@ -44,13 +44,13 @@ def crop(data, offsets, crop_shape):
 class TestCropTensorOp(OpTest):
     def setUp(self):
         self.op_type = "crop_tensor"
-        self.crop_by_1D_shape = False
+        self.shape_by_input = False
         self.offset_by_input = False
         self.unk_dim_idx = -1
         self.attrs = {}
         self.initTestCase()
 
-        if self.crop_by_1D_shape:
+        if self.shape_by_input:
             self.inputs = {
                 'X': np.random.random(self.x_shape).astype("float32"),
                 'Shape': np.array(self.crop_shape).astype("int32")
@@ -65,11 +65,11 @@ class TestCropTensorOp(OpTest):
         else:
             self.attrs['offsets'] = self.offsets
 
-        if self.unk_dim_idx != -1:
-            self.crop_shape[self.unk_dim_idx] = self.x_shape[self.unk_dim_idx]
-        self.outputs = {
-            'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
-        }
+        crop_shape = [val for val in self.crop_shape]
+        for i in range(len(self.crop_shape)):
+            if self.crop_shape[i] == -1:
+                crop_shape[i] = self.x_shape[i] - self.offsets[i]
+        self.outputs = {'Out': crop(self.inputs['X'], self.offsets, crop_shape)}
 
     def initTestCase(self):
         self.x_shape = (8, 8)
@@ -93,9 +93,8 @@ class TestCase1(TestCropTensorOp):
 class TestCase2(TestCropTensorOp):
     def initTestCase(self):
         self.x_shape = (12, 24)
-        self.crop_shape = [-1, 8]  #only the first dimension (batch) can be -1
+        self.crop_shape = [-1, 8]
         self.offsets = [0, 0]
-        self.unk_dim_idx = 0
 
 
 class TestCase3(TestCropTensorOp):
@@ -103,16 +102,15 @@ class TestCase3(TestCropTensorOp):
         self.x_shape = (4, 8, 16)
         self.crop_shape = [2, 2, 3]
         self.offsets = [1, 5, 3]
-        self.crop_by_1D_shape = True
+        self.shape_by_input = True
 
 
 class TestCase4(TestCropTensorOp):
     def initTestCase(self):
         self.x_shape = (8, 3, 6, 6)
-        self.crop_shape = [-1, 3, 4, 4]
-        self.offsets = [0, 0, 0, 0]
-        self.crop_by_1D_shape = True
-        self.unk_dim_idx = 0
+        self.crop_shape = [-1, 3, -1, 4]
+        self.offsets = [0, 0, 1, 0]
+        self.shape_by_input = True
 
 
 class TestCase5(TestCropTensorOp):
@@ -128,14 +126,13 @@ class TestCase6(TestCropTensorOp):
         self.x_shape = (2, 2, 4, 4, 4, 2)
         self.crop_shape = [1, 1, 4, 2, 2, 2]
         self.offsets = [0, 0, 0, 0, 0, 0]
-        self.crop_by_1D_shape = True
+        self.shape_by_input = True
         self.offset_by_input = True
 
 
-class TestCropTensorOp_attr_tensor(OpTest):
+class TestCropTensorOpTensorAttr(OpTest):
     def setUp(self):
         self.op_type = "crop_tensor"
-        self.mixed_type = False
         self.OffsetsTensor = False
         self.ShapeTensor = True
         self.attrs = {}
@@ -150,8 +147,7 @@ class TestCropTensorOp_attr_tensor(OpTest):
                 'X': np.random.random(self.x_shape).astype("float32"),
                 'ShapeTensor': shape_tensor
             }
-            if self.mixed_type:
-                self.attrs['shape'] = self.shape_attr
+            self.attrs['shape'] = self.shape_attr
 
         if self.OffsetsTensor:
             offsets_tensor = []
@@ -162,17 +158,21 @@ class TestCropTensorOp_attr_tensor(OpTest):
                 'X': np.random.random(self.x_shape).astype("float32"),
                 'OffsetsTensor': offsets_tensor
             }
-        else:
-            self.attrs['offsets'] = self.offsets
+            self.attrs['offsets'] = self.offsets_attr
 
-        self.outputs = {
-            'Out': crop(self.inputs['X'], self.offsets, self.crop_shape)
-        }
+        self.attrs['shape'] = self.crop_shape
+        self.attrs['offsets'] = self.offsets
+        crop_shape = [val for val in self.crop_shape]
+        for i in range(len(self.crop_shape)):
+            if self.crop_shape[i] == -1:
+                crop_shape[i] = self.x_shape[i] - self.offsets[i]
+        self.outputs = {'Out': crop(self.inputs['X'], self.offsets, crop_shape)}
 
     def initTestCase(self):
         self.x_shape = (8, 8)
         self.crop_shape = (2, 2)
         self.offsets = [1, 2]
+        self.shape_attr = [0, 0]
 
     def test_check_output(self):
         self.check_output()
@@ -181,38 +181,85 @@ class TestCropTensorOp_attr_tensor(OpTest):
         self.check_grad(["X"], "Out", max_relative_error=0.006)
 
 
-class TestCropTensorOp_attr_tensor_case1(TestCropTensorOp_attr_tensor):
-    def init_data(self):
+class TestCropTensorOpTensorAttrCase1(TestCropTensorOpTensorAttr):
+    def initTestCase(self):
         self.x_shape = (16, 8, 32)
-        self.crop_shape = [2, 2, 3]
+        self.crop_shape = [-1, -1, 3]
         self.offsets = [1, 5, 3]
+        self.shape_attr = [-1, -1, 3]
 
 
-class TestCropTensorOp_attr_tensor_case2(TestCropTensorOp_attr_tensor):
-    def init_data(self):
+class TestCropTensorOpTensorAttrCase2(TestCropTensorOpTensorAttr):
+    def initTestCase(self):
         self.x_shape = (4, 8, 16, 8)
         self.crop_shape = [2, 2, 3, 4]
         self.offsets = [1, 5, 3, 0]
-        self.shape_attr = [-1, -1, 3, 4]
-        self.mixed_type = True
+        self.shape_attr = [0, 0, 3, 4]
 
 
-class TestCropTensorOp_attr_tensor_case3(TestCropTensorOp_attr_tensor):
-    def init_data(self):
+class TestCropTensorOpTensorAttrCase3(TestCropTensorOpTensorAttr):
+    def initTestCase(self):
         self.x_shape = (16, 8, 32)
         self.crop_shape = [2, 2, 3]
         self.offsets = [1, 5, 3]
+        self.offsets_attr = [-1, -1, 3]
         self.ShapeTensor = False
         self.OffsetsTensor = True
 
 
-class TestCropTensorOp_attr_tensor_case4(TestCropTensorOp_attr_tensor):
-    def init_data(self):
+class TestCropTensorOpTensorAttrCase4(TestCropTensorOpTensorAttr):
+    def initTestCase(self):
         self.x_shape = (16, 8, 32)
         self.crop_shape = [2, 2, 3]
+        self.shape_attr = [0, 2, 3]
         self.offsets = [1, 5, 3]
+        self.offsets_attr = [-1, -1, 3]
         self.OffsetsTensor = True
 
 
+class TestCropTensorException(OpTest):
+    def test_exception(self):
+        input1 = fluid.data(name="input1", shape=[2, 3, 6, 6], dtype="float32")
+        input2 = fluid.data(name="input2", shape=[2, 3, 6, 6], dtype="float16")
+        dim = fluid.data(name='dim', shape=[1], dtype='int32')
+        offset = fluid.data(name='offset', shape=[1], dtype='int32')
+
+        def attr_shape_type():
+            out = fluid.layers.crop_tensor(input1, shape=3)
+
+        def attr_shape_dtype():
+            out = fluid.layers.crop_tensor(input1, shape=[2, 2.0, 3, 3])
+
+        def attr_shape_value1():
+            out = fluid.layers.crop_tensor(input1, shape=[2, -2, dim, 3])
+
+        def attr_shape_value2():
+            out = fluid.layers.crop_tensor(input1, shape=[2, 0, dim, 3])
+
+        def attr_offsets_type():
+            out = fluid.layers.crop_tensor(
+                input1, shape=[2, 2, 3, 3], offsets=0)
+
+        def attr_offsets_dtype():
+            out = fluid.layers.crop_tensor(
+                input1, shape=[2, 2, 3, 3], offsets=[0, 1.0, 0, 0])
+
+        def attr_offsets_value():
+            out = fluid.layers.crop_tensor(
+                input1, shape=[2, 2, 3, 3], offsets=[0, -1, offset, 0])
+
+        def input_dtype():
+            out = fluid.layers.crop_tensor(input2, shape=[2, 2, 3, 3])
+
+        self.assertRaises(TypeError, attr_shape_type)
+        self.assertRaises(TypeError, attr_shape_dtype)
+        self.assertRaises(ValueError, attr_shape_value1)
+        self.assertRaises(ValueError, attr_shape_value2)
+        self.assertRaises(TypeError, attr_offsets_type)
+        self.assertRaises(TypeError, attr_offsets_dtype)
+        self.assertRaises(ValueError, attr_offsets_value)
+        self.assertRaises(TypeError, input_dtype)
+
+
 if __name__ == '__main__':
     unittest.main()