diff --git a/paddle/fluid/operators/cross_entropy_op.cc b/paddle/fluid/operators/cross_entropy_op.cc index 880ea0d96ce8d077eea19d2640101603a59db90a..ca24261bcc84e2d476891ef5ab7b89a981437b36 100644 --- a/paddle/fluid/operators/cross_entropy_op.cc +++ b/paddle/fluid/operators/cross_entropy_op.cc @@ -25,12 +25,9 @@ class CrossEntropyOpBase : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, "Input(X) should be not null."); - PADDLE_ENFORCE_EQ(ctx->HasInput("Label"), true, - "Input(Label) should be not null."); - - PADDLE_ENFORCE_EQ(ctx->HasOutput("Y"), true, - "Output(Y) should be not null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CrossEntropy"); + OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "CrossEntropy"); + OP_INOUT_CHECK(ctx->HasOutput("Y"), "Output", "Y", "CrossEntropy"); auto x_dims = ctx->GetInputDim("X"); auto label_dims = ctx->GetInputDim("Label"); @@ -44,53 +41,61 @@ class CrossEntropyOpBase : public framework::OperatorWithKernel { PADDLE_ENFORCE_EQ( framework::slice_ddim(x_dims, 0, rank - 1), framework::slice_ddim(label_dims, 0, rank - 1), - "ShapeError: Input(X) and Input(Label) shall have the same shape " - "except the last dimension. But received: the shape of Input(X) is " - "[%s]," - "the shape of Input(Label) is [%s].", - x_dims, label_dims); + platform::errors::InvalidArgument( + "Input(X) and Input(Label) shall have the same shape " + "except the last dimension. But received: the shape of Input(X) " + "is " + "[%s], the shape of Input(Label) is [%s].", + x_dims, label_dims)); } if (IsSoftLabel(ctx)) { PADDLE_ENFORCE_EQ( rank, label_dims.size(), - "ShapeError: If Attr(soft_label) == true, Input(X) and Input(Label) " - "shall have the same dimensions. But received: the dimensions of " - "Input(X) is [%d]," - "the shape of Input(X) is [%s], the dimensions of Input(Label) is " - "[%d], the shape of" - "Input(Label) is [%s]", - rank, x_dims, label_dims.size(), label_dims); + platform::errors::InvalidArgument( + "If Attr(soft_label) == true, Input(X) and Input(Label) " + "shall have the same dimensions. But received: the dimensions of " + "Input(X) is [%d]," + "the shape of Input(X) is [%s], the dimensions of Input(Label) " + "is " + "[%d], the shape of" + "Input(Label) is [%s]", + rank, x_dims, label_dims.size(), label_dims)); if (check) { PADDLE_ENFORCE_EQ( x_dims[rank - 1], label_dims[rank - 1], - "ShapeError: If Attr(soft_label) == true, the last dimension of " - "Input(X) and Input(Label) should be equal. But received: the" - "last dimension of Input(X) is [%d], the shape of Input(X) is [%s]," - "the last dimension of Input(Label) is [%d], the shape of " - "Input(Label)" - "is [%s], the last dimension is [%d].", - x_dims[rank - 1], x_dims, label_dims[rank - 1], label_dims, - rank - 1); + platform::errors::InvalidArgument( + "If Attr(soft_label) == true, the last dimension of " + "Input(X) and Input(Label) should be equal. But received: the" + "last dimension of Input(X) is [%d], the shape of Input(X) is " + "[%s]," + "the last dimension of Input(Label) is [%d], the shape of " + "Input(Label)" + "is [%s], the last dimension is [%d].", + x_dims[rank - 1], x_dims, label_dims[rank - 1], label_dims, + rank - 1)); } } else { if (rank == label_dims.size()) { PADDLE_ENFORCE_EQ( label_dims[rank - 1], 1UL, - "ShapeError: the last dimension of Input(Label) should be 1." - "But received: the last dimension of Input(Label) is [%d]," - "the last dimension is [%d]", - label_dims[rank - 1], rank - 1); + platform::errors::InvalidArgument( + "the last dimension of Input(Label) should be 1." + "But received: the last dimension of Input(Label) is [%d]," + "the last dimension is [%d]", + label_dims[rank - 1], rank - 1)); } else { - PADDLE_ENFORCE_EQ(rank, label_dims.size() + 1, - "ShapeError: The rank of Input(X) should be equal to " - "Input(Label) plus 1." - "But received: The dimension of Input(X) is [%d], " - "the shape of Input(X) is [%s]," - "the dimension of Input(Label) is [%d], the shape of " - "Input(Label) is [%s]", - rank, x_dims, label_dims.size(), label_dims); + PADDLE_ENFORCE_EQ( + rank, label_dims.size() + 1, + platform::errors::InvalidArgument( + "ShapeError: The rank of Input(X) should be equal to " + "Input(Label) plus 1." + "But received: The dimension of Input(X) is [%d], " + "the shape of Input(X) is [%s]," + "the dimension of Input(Label) is [%d], the shape of " + "Input(Label) is [%s]", + rank, x_dims, label_dims.size(), label_dims)); } } @@ -122,19 +127,23 @@ class CrossEntropyGradientOpBase : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const { - PADDLE_ENFORCE_EQ(ctx->HasInput("Label"), true, - "Input(Label) should be not null."); - PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Y")), true, - "Input(Y@GRAD) shoudl be not null."); - PADDLE_ENFORCE_EQ(ctx->HasOutput(framework::GradVarName("X")), true, - "Output(X@GRAD) should be not null."); + OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", + "CrossEntropyGradientOpBase"); + OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), "Input", + framework::GradVarName("Y"), "CrossEntropyGradientOpBase"); + OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output", + framework::GradVarName("X"), "CrossEntropyGradientOpBase"); auto x_dims = GetXDim(ctx); auto label_dims = ctx->GetInputDim("Label"); auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y")); int rank = x_dims.size(); - PADDLE_ENFORCE_EQ(dy_dims.size(), label_dims.size(), - "Input(Y@Grad) and Input(Y) should have the same rank."); + PADDLE_ENFORCE_EQ( + dy_dims.size(), label_dims.size(), + platform::errors::InvalidArgument( + "Input(Y@Grad) and Input(Y) should have the same rank." + "But received: Y@Grad's rank is [%d], Y's rank is [%d]", + dy_dims.size(), label_dims.size())); bool check = true; if ((!ctx->IsRuntime()) && @@ -143,10 +152,15 @@ class CrossEntropyGradientOpBase : public framework::OperatorWithKernel { } if (check) { - PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1), - framework::slice_ddim(dy_dims, 0, rank - 1), - "The Input(X) and Input(Y@Grad) should have the same " - "shape except the last dimension."); + PADDLE_ENFORCE_EQ( + framework::slice_ddim(x_dims, 0, rank - 1), + framework::slice_ddim(dy_dims, 0, rank - 1), + platform::errors::InvalidArgument( + "The Input(X) and Input(Y@Grad) should have the same " + "shape except the last dimension. but received: " + "the shape of Input(X) is [%s], " + "the shape of Input(Y@Grad) is [%s].", + x_dims, dy_dims)); } ctx->SetOutputDim(framework::GradVarName("X"), x_dims); @@ -253,7 +267,7 @@ class CrossEntropyGradientOp : public CrossEntropyGradientOpBase { using CrossEntropyGradientOpBase::CrossEntropyGradientOpBase; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true, "Input(X) should be not null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "CrossEntropyGradientOp"); CrossEntropyGradientOpBase::InferShape(ctx); } }; @@ -281,11 +295,10 @@ class CrossEntropyOp2 : public CrossEntropyOpBase { void InferShape(framework::InferShapeContext* ctx) const override { CrossEntropyOpBase::InferShape(ctx); - PADDLE_ENFORCE_EQ(ctx->HasOutput("XShape"), true, - "Output(XShape) should be not null."); - - PADDLE_ENFORCE_EQ(ctx->HasOutput("MatchX"), true, - "Output(MatchX) should be not null."); + OP_INOUT_CHECK(ctx->HasOutput("XShape"), "Output", "XShape", + "CrossEntropyOp2"); + OP_INOUT_CHECK(ctx->HasOutput("MatchX"), "Output", "MatchX", + "CrossEntropyOp2"); auto x_dims = ctx->GetInputDim("X"); auto x_dims_vec = framework::vectorize(x_dims); x_dims_vec.push_back(0); @@ -305,8 +318,8 @@ class CrossEntropyGradientOp2 : public CrossEntropyGradientOpBase { public: using CrossEntropyGradientOpBase::CrossEntropyGradientOpBase; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE_EQ(ctx->HasInput("MatchX"), true, - "Input(MatchX) must exist"); + OP_INOUT_CHECK(ctx->HasInput("MatchX"), "Input", "MatchX", + "CrossEntropyGradientOp2"); CrossEntropyGradientOpBase::InferShape(ctx); } diff --git a/paddle/fluid/operators/cross_entropy_op.h b/paddle/fluid/operators/cross_entropy_op.h index 667135c4f8d145cdba4255dab0f8075489b68d6d..8424fc4376fd706222606fb4b87c59c675e7c71f 100644 --- a/paddle/fluid/operators/cross_entropy_op.h +++ b/paddle/fluid/operators/cross_entropy_op.h @@ -166,11 +166,14 @@ struct HardLabelCrossEntropyForwardFunctor { HOSTDEVICE void operator()(int64_t idx) const { auto label = label_[idx]; if (label != ignore_index_) { + // don't update to PADDLE_ENFORCE_GE and PADDLE_ENFORCE_LT cause + // can't use platform::errors::InvalidArgument in HOSTDEVICE PADDLE_ENFORCE(label >= 0 && label < feature_size_, "Variable value (label) of " "OP(fluid.layers.cross_entropy) expected >= 0 " "and < %ld, but got %ld. Please check label value.", feature_size_, label); + auto match_x = x_[idx * feature_size_ + label]; y_[idx] = -math::TolerableValue()(real_log(match_x)); match_x_[idx] = match_x; diff --git a/paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc b/paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc index f1b22a86eb56f01bad635bd739f2405b0e708207..a85f600003138c0329e5c806ef0f4977aa910ef5 100644 --- a/paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc +++ b/paddle/fluid/operators/sigmoid_cross_entropy_with_logits_op.cc @@ -28,16 +28,24 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel { using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); - PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null."); - PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", + "SigmoidCrossEntropyWithLogitsOp"); + OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", + "SigmoidCrossEntropyWithLogitsOp"); + OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", + "SigmoidCrossEntropyWithLogitsOp"); auto x_dims = ctx->GetInputDim("X"); auto labels_dims = ctx->GetInputDim("Label"); int rank = x_dims.size(); PADDLE_ENFORCE_EQ(rank, labels_dims.size(), - "Input(X) and Input(Label) shall have the same rank."); + platform::errors::InvalidArgument( + "Input(X) and Input(Label) shall have the same rank." + "But received: the rank of Input(X) is [%d], " + "the rank of Input(Label) is [%d].", + rank, labels_dims.size())); + bool check = true; if ((!ctx->IsRuntime()) && (framework::product(x_dims) <= 0 || framework::product(labels_dims) <= 0)) { @@ -45,10 +53,14 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel { } if (check) { - PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank), - framework::slice_ddim(labels_dims, 0, rank), - "Input(X) and Input(Label) shall have the same shape " - "except the last dimension."); + PADDLE_ENFORCE_EQ( + framework::slice_ddim(x_dims, 0, rank), + framework::slice_ddim(labels_dims, 0, rank), + platform::errors::InvalidArgument( + "Input(X) and Input(Label) shall have the same shape " + "except the last dimension. But received: the shape of " + "Input(X) is [%s], the shape of Input(Label) is [%s].", + x_dims, labels_dims)); } ctx->ShareDim("X", /*->*/ "Out"); @@ -62,12 +74,16 @@ class SigmoidCrossEntropyWithLogitsGradOp using framework::OperatorWithKernel::OperatorWithKernel; void InferShape(framework::InferShapeContext* ctx) const override { - PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null."); - PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null."); - PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), - "Input(Out@GRAD) shoudl be not null."); - PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")), - "Output(X@GRAD) should be not null."); + OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", + "SigmoidCrossEntropyWithLogitsGradOp"); + OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", + "SigmoidCrossEntropyWithLogitsGradOp"); + OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input", + framework::GradVarName("Out"), + "SigmoidCrossEntropyWithLogitsGradOp"); + OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output", + framework::GradVarName("X"), + "SigmoidCrossEntropyWithLogitsGradOp"); auto x_dims = ctx->GetInputDim("X"); auto labels_dims = ctx->GetInputDim("Label"); @@ -81,14 +97,23 @@ class SigmoidCrossEntropyWithLogitsGradOp } if (check) { - PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank), - framework::slice_ddim(labels_dims, 0, rank), - "Input(X) and Input(Label) shall have the same shape."); + PADDLE_ENFORCE_EQ( + framework::slice_ddim(x_dims, 0, rank), + framework::slice_ddim(labels_dims, 0, rank), + platform::errors::InvalidArgument( + "Input(X) and Input(Label) shall have the same shape " + "except the last dimension. But received: the shape of " + "Input(X) is [%s], the shape of Input(Label) is [%s].", + x_dims, labels_dims)); PADDLE_ENFORCE_EQ( framework::slice_ddim(x_dims, 0, rank), framework::slice_ddim(dout_dims, 0, rank), - "Input(X) and Input(Out@Grad) shall have the same shape."); + platform::errors::InvalidArgument( + "Input(X) and Input(Out@Grad) shall have the same shape " + "except the last dimension. But received: the shape of " + "Input(X) is [%s], the shape of Input(Out@Grad) is [%s].", + x_dims, dout_dims)); } ctx->SetOutputDim(framework::GradVarName("X"), x_dims); diff --git a/python/paddle/fluid/layers/loss.py b/python/paddle/fluid/layers/loss.py index 9b6f2235ef5bf8860fa85b3f81d99e498fa40a0c..0281c0433f54b157c0ab4c882416eec88558f545 100644 --- a/python/paddle/fluid/layers/loss.py +++ b/python/paddle/fluid/layers/loss.py @@ -1410,9 +1410,14 @@ def sigmoid_cross_entropy_with_logits(x, ${comment} Args: - x(${x_type}): ${x_comment} - label(${label_type}): ${label_comment} - ignore_index(int): ${ignore_index_comment} + x(Variable): a 2-D tensor with shape N x D, where N is the batch size and + D is the number of classes. This input is a tensor of logits computed + by the previous operator. Logits are unscaled log probabilities given + as log(p/(1-p)) The data type should be float32 or float64. + label (Variable): a 2-D tensor of the same type and shape as X. + This input is a tensor of probabalistic labels for each logit. + ignore_index(int): Specifies a target value that is ignored and + does not contribute to the input gradient. name(str|None): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` @@ -1437,6 +1442,8 @@ def sigmoid_cross_entropy_with_logits(x, normalize=True) # or False # loss = fluid.layers.reduce_sum(loss) # summation of loss """ + check_variable_and_dtype(x, 'input', ['float16', 'float32', 'float64'], + 'sigmoid_cross_entropy_with_logits') helper = LayerHelper("sigmoid_cross_entropy_with_logits", **locals()) diff --git a/python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py b/python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py index e1f7e9c03f2d546630a6d0b4dd2326cf9b2ebef4..51751588f7b94447080f80002ceb29dac2429529 100644 --- a/python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py +++ b/python/paddle/fluid/tests/unittests/test_sigmoid_cross_entropy_with_logits_op.py @@ -20,6 +20,8 @@ from scipy.special import logit from scipy.special import expit import paddle.fluid.core as core import unittest +from paddle.fluid import compiler, Program, program_guard +import paddle.fluid as fluid class TestSigmoidCrossEntropyWithLogitsOp1(OpTest): @@ -242,5 +244,31 @@ class TestSigmoidCrossEntropyWithLogitsOp6(OpTest): self.check_grad(['X'], 'Out') +class TestSigmoidCrossEntropyWithLogitsOpError(unittest.TestCase): + def test_errors(self): + with program_guard(Program(), Program()): + + def test_Variable(): + # the input of sigmoid_cross_entropy_with_logits must be Variable. + x1 = fluid.create_lod_tensor( + np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()) + lab1 = fluid.create_lod_tensor( + np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()) + fluid.layers.sigmoid_cross_entropy_with_logits(x1, lab1) + + self.assertRaises(TypeError, test_Variable) + + def test_dtype(): + # the input dtype of sigmoid_cross_entropy_with_logits must be float16 or float32 or float64 + # float16 only can be set on GPU place + x2 = fluid.layers.data( + name='x2', shape=[3, 4, 5, 6], dtype="int32") + lab2 = fluid.layers.data( + name='lab2', shape=[3, 4, 5, 6], dtype="int32") + fluid.layers.sigmoid_cross_entropy_with_logits(x2, lab2) + + self.assertRaises(TypeError, test_dtype) + + if __name__ == '__main__': unittest.main()