diff --git a/paddle/fluid/operators/select_input_op.cc b/paddle/fluid/operators/select_input_op.cc index fa0c3ac04d236d992265a4395676bfa407e74ba6..c35d18178fc31e0f1144fb3a121db1e2f99f225b 100644 --- a/paddle/fluid/operators/select_input_op.cc +++ b/paddle/fluid/operators/select_input_op.cc @@ -40,9 +40,13 @@ class SelectInputOp : public framework::OperatorBase { size_t output_branch = static_cast(GetBranchNumber(mask)); const std::vector &x_names = Inputs("X"); - PADDLE_ENFORCE_LT(output_branch, x_names.size(), - "Selected branch number is greater than actual branch " - "num in SelectInputOp"); + PADDLE_ENFORCE_LT( + output_branch, x_names.size(), + platform::errors::InvalidArgument( + "Input 'Mask' in SelectInputOp is invalid. " + "'Mask' must be less than the size of input vector 'X'. " + "But received Mask = %d, X's size = %d.", + output_branch, x_names.size())); const framework::Variable *selected_x = scope.FindVar(x_names[output_branch]); diff --git a/paddle/fluid/operators/select_op_helper.h b/paddle/fluid/operators/select_op_helper.h index a159530d2a345d0f68342dc57c882fcbb843b318..5df4f8c4a543bdeb92dcea1bdb6809db86132d45 100644 --- a/paddle/fluid/operators/select_op_helper.h +++ b/paddle/fluid/operators/select_op_helper.h @@ -27,7 +27,11 @@ namespace operators { // selected branch number. inline int GetBranchNumber(const framework::LoDTensor &mask) { PADDLE_ENFORCE_EQ(mask.numel(), 1, - "Mask in SelectOutputOp must have numel 1."); + platform::errors::InvalidArgument( + "The numel of Input(Mask) in SelectInputOp or " + "SelectOutputOp must be 1. " + "But received %d, and it's shape is [%s].", + mask.numel(), mask.dims())); if (platform::is_cpu_place(mask.place())) { return mask.data()[0]; } @@ -36,9 +40,10 @@ inline int GetBranchNumber(const framework::LoDTensor &mask) { #ifdef PADDLE_WITH_CUDA framework::TensorCopySync(mask, platform::CPUPlace(), cpu_mask.get()); #else - PADDLE_THROW( - "This version of PaddlePaddle doen NOT support GPU but got GPU tensor " - "Mask in SelectOutputOp. Please compile WITH_GPU option"); + PADDLE_THROW(platform::errors::PreconditionNotMet( + "This version of PaddlePaddle does NOT support GPU, " + "but got GPU tensor 'Mask' in SelectInputOp or SelectOutputOp. " + "Please compile PaddlePaddle WITH_GPU first.")); #endif return cpu_mask->data()[0]; } diff --git a/paddle/fluid/operators/select_output_op.cc b/paddle/fluid/operators/select_output_op.cc index 2db2c75396e3cd8f5fdfd5ed0beb5574539d1feb..8885c67295af5050a3d2e6894a6e713a0017181b 100644 --- a/paddle/fluid/operators/select_output_op.cc +++ b/paddle/fluid/operators/select_output_op.cc @@ -41,9 +41,13 @@ class SelectOutputOp : public framework::OperatorBase { size_t output_branch = static_cast(GetBranchNumber(mask)); const std::vector &out_names = Outputs("Out"); - PADDLE_ENFORCE_LT(output_branch, out_names.size(), - "Selected branch number is greater than actual branch " - "num in SelectOutputOp"); + PADDLE_ENFORCE_LT( + output_branch, out_names.size(), + platform::errors::InvalidArgument( + "Input 'Mask' in SelectOutputOp is invalid. " + "'Mask' must be less than the size of output vector 'Out'. " + "But received Mask = %d, Out's size = %d.", + output_branch, out_names.size())); const framework::Variable *x = scope.FindVar(Input("X")); framework::Variable *selected_out = scope.FindVar(out_names[output_branch]); diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py index 63029d2f1e549750746b0c36af0b267505b94d11..07211bdd0c95d60e9cdfe97a15e40f9c472e424b 100755 --- a/python/paddle/fluid/layers/control_flow.py +++ b/python/paddle/fluid/layers/control_flow.py @@ -56,6 +56,10 @@ def select_output(input, outputs, mask): Variable: The outputs variables """ helper = LayerHelper('select_output', **locals()) + check_type(input, 'input', (Variable), 'select_output') + check_variable_and_dtype(mask, 'mask', ['int32'], 'select_output') + check_type(outputs, 'outputs', (list, tuple), 'select_output') + helper.append_op( type='select_output', inputs={'X': input, @@ -80,14 +84,12 @@ def select_input(inputs, mask): Variable: The selected input variable """ helper = LayerHelper('select_input', **locals()) - if isinstance(inputs, list) or isinstance(inputs, tuple): - input_dtype = inputs[0].dtype - input_shape = inputs[0].shape - input_type = inputs[0].type - else: - input_dtype = inputs.dtype - input_shape = inputs.shape - input_type = inputs.type + check_type(inputs, 'inputs', (list, tuple), 'select_input') + check_variable_and_dtype(mask, 'mask', ['int32'], 'select_input') + + input_dtype = inputs[0].dtype + input_shape = inputs[0].shape + input_type = inputs[0].type out = helper.create_variable( dtype=input_dtype, shape=input_shape, type=input_type) diff --git a/python/paddle/fluid/tests/unittests/test_select_input_output_op.py b/python/paddle/fluid/tests/unittests/test_select_input_output_op.py index bd66bf81b061cf228b427703a4622927ae3ab30f..23b394516fc13cdc9b5ccd7ce9c641ea191b517a 100644 --- a/python/paddle/fluid/tests/unittests/test_select_input_output_op.py +++ b/python/paddle/fluid/tests/unittests/test_select_input_output_op.py @@ -60,34 +60,67 @@ class TestSplitMergeSelectedVarOps(unittest.TestCase): self.assertTrue(np.allclose(np.asarray(ret[0]), feed_x)) self.assertTrue(np.allclose(np.asarray(ret[1]), x_grad)) - def test_forward_backward_single_tensor_output(self): - program = Program() - with program_guard(program): - x = layers.data(name='x', shape=[2], dtype='float32') - x.stop_gradient = False # For test gradient + +class TestSelectInputOpError(unittest.TestCase): + def test_errors(self): + with program_guard(Program(), Program()): mask = layers.data(name='mask', shape=[1], dtype='int32') + in1 = layers.data(name='in1', shape=[1], dtype='int32') + + # 1. The type of inputs in select_input must be list or tuple. + def test_inputs_type(): + select_input(1, mask) + + self.assertRaises(TypeError, test_inputs_type) + + # 2. The type of mask in select_input must be Variable. + def test_mask_type(): + select_input([in1], mask=1) + + self.assertRaises(TypeError, test_mask_type) + + # 3. The dtype of mask in select_input must be int32 or int64. + def test_mask_dtype(): + mask = layers.data(name='mask2', shape=[1], dtype='float32') + select_input([in1], mask) + + self.assertRaises(TypeError, test_mask_dtype) + + +class TestSelectOutput_Error(unittest.TestCase): + def test_errors(self): + with program_guard(Program(), Program()): + + in1 = layers.data(name='in1', shape=[1], dtype='int32') + mask_int32 = layers.data( + name='mask_int32', shape=[1], dtype='int32') + mask_float32 = layers.data( + name='mask_float32', shape=[1], dtype='float32') + out1 = layers.data(name='out1', shape=[1], dtype='int32') + + # 1. The type of input in select_output must Variable. + def test_input_type(): + select_output(1, [out1], mask_int32) + + self.assertRaises(TypeError, test_input_type) + + # 2. The type of mask in select_output must be Variable. + def test_mask_type(): + select_output(in1, [out1], mask=1) + + self.assertRaises(TypeError, test_mask_type) + + # 3. The dtype of mask in select_output must be int32 or int64. + def test_mask_dtype(): + select_output(in1, [out1], mask=mask_float32) + + self.assertRaises(TypeError, test_mask_dtype) + + # 4. The type of mask in select_output must be list or tuple. + def test_outputs_type(): + select_output(in1, out1, mask=mask_int32) - out = program.current_block().create_var( - dtype='float32', type=core.VarDesc.VarType.LOD_TENSOR) - - select_output(x, out, mask) - y = select_input(out, mask) - mean = layers.mean(y) - append_backward(mean) - - place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda( - ) else fluid.CPUPlace() - exe = Executor(place) - - feed_x = np.asarray([1.3, -1.4]).astype(np.float32) - feed_mask = np.asarray([0]).astype(np.int32) - ret = exe.run(program, - feed={'x': feed_x, - 'mask': feed_mask}, - fetch_list=[y.name, x.grad_name]) - x_grad = np.asarray([0.5, 0.5]).astype(np.float32) - self.assertTrue(np.allclose(np.asarray(ret[0]), feed_x)) - self.assertTrue(np.allclose(np.asarray(ret[1]), x_grad)) + self.assertRaises(TypeError, test_outputs_type) if __name__ == '__main__':