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Op(prelu/relu/f.relu/f.log_softmax) error message enhancement (#23792)

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revert-22778-infer_var_type
zhupengyang 5 years ago committed by GitHub
parent c2a60bb1fa
commit 56c54cccbd
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 75 additions and 52 deletions
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15
paddle/fluid/operators/prelu_op.cc
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@ -31,10 +31,11 @@ class PReluOp : public framework::OperatorWithKernel {
auto x_dim = ctx->GetInputDim("X");
std::string mode = ctx->Attrs().Get<std::string>("mode");
if (mode == "all") {
PADDLE_ENFORCE_EQ(
product(ctx->GetInputDim("Alpha")), 1,
platform::errors::InvalidArgument(
"For mode 'all', size of weight Alpha must be one."));
PADDLE_ENFORCE_EQ(product(ctx->GetInputDim("Alpha")), 1,
platform::errors::InvalidArgument(
"For mode 'all', size of weight Alpha must be one. "
"But recevied alpha's size: %d.",
product(ctx->GetInputDim("Alpha"))));
} else if (mode == "channel") {
PADDLE_ENFORCE_EQ(product(ctx->GetInputDim("Alpha")), x_dim[1],
platform::errors::InvalidArgument(
@ -67,7 +68,11 @@ class PReluOp : public framework::OperatorWithKernel {
"x's size: %d.",
alpha_product, x_product));
} else {
PADDLE_THROW("Unkown mode %s", mode);
PADDLE_THROW(platform::errors::InvalidArgument(
"Attr(mode) of prelu must be one of 'all', 'channel', or 'element'. "
"But recevied "
"mode: '%s'.",
mode));
}
ctx->ShareDim("X", /*->*/ "Out");
ctx->ShareLoD("X", /*->*/ "Out");

2
python/paddle/fluid/tests/unittests/test_activation_op.py
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@ -463,7 +463,7 @@ class TestReluOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.sqrt, 1)
self.assertRaises(TypeError, fluid.layers.relu, 1)
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.relu, x_int32)

99
python/paddle/fluid/tests/unittests/test_prelu_op.py
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@ -23,21 +23,18 @@ from paddle.fluid import Program, program_guard
from op_test import OpTest, skip_check_grad_ci
class TestPReluAPIError(unittest.TestCase):
class TestPReluOpError(unittest.TestCase):
def test_errors(self):
with fluid.program_guard(fluid.Program(), fluid.Program()):
layer = fluid.PRelu(
mode='all',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(1.0)))
# the input must be Variable.
x0 = fluid.create_lod_tensor(
np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace())
self.assertRaises(TypeError, layer, x0)
# the input dtype must be float32
data_t = fluid.data(
name="input", shape=[5, 200, 100, 100], dtype="float64")
self.assertRaises(TypeError, layer, data_t)
with program_guard(Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.prelu, 0.1, 'all')
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.prelu, x_int32, 'all')
# support the input dtype is float32
x_fp16 = fluid.layers.data(
name='x_fp16', shape=[12, 10], dtype='float32')
fluid.layers.prelu(x_fp16, 'all')
class PReluTest(OpTest):
@ -79,39 +76,55 @@ class PReluTest(OpTest):
self.check_grad(['X', 'Alpha'], 'Out')
# TODO(minqiyang): Resume these test cases after fixing Python3 CI job issues
if six.PY2:
@skip_check_grad_ci(
reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
)
class TestModeAll(PReluTest):
def init_input_shape(self):
self.x_shape = (2, 3, 4, 5)
def init_attr(self):
self.attrs = {'mode': "all"}
@skip_check_grad_ci(
reason="[skip shape check] Input(Alpha) must be 1-D and only has one data in 'all' mode"
)
class TestModeAll(PReluTest):
def init_input_shape(self):
self.x_shape = (2, 3, 4, 5)
class TestModeElt(PReluTest):
def init_input_shape(self):
self.x_shape = (3, 2, 5, 10)
def init_attr(self):
self.attrs = {'mode': "all"}
def init_attr(self):
self.attrs = {'mode': "element"}
class TestModeElt(PReluTest):
def init_input_shape(self):
self.x_shape = (3, 2, 5, 10)
class TestPReluOpError(unittest.TestCase):
def test_errors(self):
with program_guard(Program()):
# The input type must be Variable.
self.assertRaises(TypeError, fluid.layers.prelu, 1, 'all')
# The input dtype must be float16, float32, float64.
x_int32 = fluid.data(name='x_int32', shape=[12, 10], dtype='int32')
self.assertRaises(TypeError, fluid.layers.prelu, x_int32, 'all')
# support the input dtype is float32
x_fp16 = fluid.layers.data(
name='x_fp16', shape=[12, 10], dtype='float32')
fluid.layers.prelu(x_fp16, 'all')
def init_attr(self):
self.attrs = {'mode': "element"}
def prelu_t(x, mode, param_attr=None, name=None):
helper = fluid.layer_helper.LayerHelper('prelu', **locals())
alpha_shape = [1, x.shape[1], 1, 1]
dtype = helper.input_dtype(input_param_name='x')
alpha = helper.create_parameter(
attr=helper.param_attr,
shape=alpha_shape,
dtype='float32',
is_bias=False,
default_initializer=fluid.initializer.ConstantInitializer(0.25))
out = helper.create_variable_for_type_inference(dtype)
helper.append_op(
type="prelu",
inputs={"X": x,
'Alpha': alpha},
attrs={"mode": mode},
outputs={"Out": out})
return out
# error message test if mode is not one of 'all', 'channel', 'element'
class TestModeError(unittest.TestCase):
def test_mode_error(self):
main_program = Program()
with fluid.program_guard(main_program, Program()):
x = fluid.data(name='x', shape=[2, 3, 4, 5])
try:
y = prelu_t(x, 'any')
except Exception as e:
assert (e.args[0].find('InvalidArgumentError') != -1)
if __name__ == "__main__":

11
python/paddle/nn/functional/activation.py
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@ -206,8 +206,10 @@ def relu(input, inplace=False, name=None):
)
return core.ops.relu(input)
helper = LayerHelper('relu', **locals())
check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'],
'relu')
helper = LayerHelper('relu', **locals())
outs = input if inplace else helper.create_variable_for_type_inference(
input.dtype)
helper.append_op(type='relu', inputs={'X': [input]}, outputs={'Out': outs})
@ -263,7 +265,7 @@ def sigmoid(input, inplace=False, name=None):
)
return core.ops.sigmoid(input)
check_variable_and_dtype(input, 'X', ['float16', 'float32', 'float64'],
check_variable_and_dtype(input, 'input', ['float16', 'float32', 'float64'],
'sigmoid')
helper = LayerHelper("sigmoid", **locals())
outputs = helper.create_variable_for_type_inference(input.dtype)
@ -329,8 +331,11 @@ def log_softmax(input, axis=None, dtype=None, name=None):
False)
return core.ops.log(outs_softmax)
helper = LayerHelper("log_softmax", **locals())
if dtype is None:
check_variable_and_dtype(
input, 'input', ['float16', 'float32', 'float64'], 'log_softmax')
helper = LayerHelper("log_softmax", **locals())
outs_cast = input
if dtype is not None:
outs_cast = helper.create_variable_for_type_inference(dtype)

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