提交 c5b556f0 编写于 作者: D daming-lu

Merge branch 'develop' into fix_deploy_script

...@@ -90,11 +90,13 @@ class LookupTableGradKernel : public framework::OpKernel<T> { ...@@ -90,11 +90,13 @@ class LookupTableGradKernel : public framework::OpKernel<T> {
auto* d_output_data = d_output->data<T>(); auto* d_output_data = d_output->data<T>();
auto* d_table_data = d_table->mutable_data<T>(context.GetPlace()); auto* d_table_data = d_table->mutable_data<T>(context.GetPlace());
memset(d_table_data, 0, d_table->numel() * sizeof(T));
for (int64_t i = 0; i < ids->numel(); ++i) { for (int64_t i = 0; i < ids->numel(); ++i) {
PADDLE_ENFORCE_LT(ids_data[i], N); PADDLE_ENFORCE_LT(ids_data[i], N);
PADDLE_ENFORCE_GE(ids_data[i], 0); PADDLE_ENFORCE_GE(ids_data[i], 0);
for (int j = 0; j < D; ++j) { for (int j = 0; j < D; ++j) {
d_table_data[ids_data[i] * D + j] = d_output_data[i * D + j]; d_table_data[ids_data[i] * D + j] += d_output_data[i * D + j];
} }
} }
} }
......
...@@ -42,7 +42,8 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker { ...@@ -42,7 +42,8 @@ class SequencePoolOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<std::string>( AddAttr<std::string>(
"pooltype", "pooltype",
"(int, default AVERAGE) the pooling pooltype of SequencePoolOp.") "(int, default AVERAGE) the pooling pooltype of SequencePoolOp.")
.SetDefault("AVERAGE"); .SetDefault("AVERAGE")
.InEnum({"AVERAGE", "SUM", "SQRT", "LAST", "FIRST", "MAX"});
AddComment(R"DOC( AddComment(R"DOC(
SequencePoolOp pools features of all time-steps of each instance. SequencePoolOp pools features of all time-steps of each instance.
......
...@@ -278,6 +278,7 @@ def sequence_conv(input, ...@@ -278,6 +278,7 @@ def sequence_conv(input,
num_filters, num_filters,
filter_size=3, filter_size=3,
filter_stride=1, filter_stride=1,
act=None,
padding=None, padding=None,
bias_attr=None, bias_attr=None,
param_attr=None, param_attr=None,
...@@ -304,7 +305,7 @@ def sequence_conv(input, ...@@ -304,7 +305,7 @@ def sequence_conv(input,
outputs={"Out": pre_bias}, outputs={"Out": pre_bias},
attrs={ attrs={
'contextStride': filter_stride, 'contextStride': filter_stride,
'contextStart': 0, 'contextStart': -int(filter_size / 2),
'contextLength': filter_size 'contextLength': filter_size
}) })
pre_act = helper.append_bias_op(pre_bias) pre_act = helper.append_bias_op(pre_bias)
...@@ -364,11 +365,6 @@ def conv2d(input, ...@@ -364,11 +365,6 @@ def conv2d(input,
def sequence_pool(input, pool_type, **kwargs): def sequence_pool(input, pool_type, **kwargs):
ENUM_POOL_TYPE = set(["MAX", "AVG", "SQRT", "LAST", "FIRST"])
if pool_type.upper() not in ENUM_POOL_TYPE:
raise ValueError("Unknown pool_type: '%s'. It can only be %s.",
str(pool_type), " ".join(ENUM_POOL_TYPE))
helper = LayerHelper('sequence_pool', input=input, **kwargs) helper = LayerHelper('sequence_pool', input=input, **kwargs)
dtype = helper.input_dtype() dtype = helper.input_dtype()
pool_out = helper.create_tmp_variable(dtype) pool_out = helper.create_tmp_variable(dtype)
......
...@@ -47,7 +47,7 @@ def img_conv_group(input, ...@@ -47,7 +47,7 @@ def img_conv_group(input,
""" """
tmp = input tmp = input
assert isinstance(conv_num_filter, list) or \ assert isinstance(conv_num_filter, list) or \
isinstance(conv_num_filter, tuple) isinstance(conv_num_filter, tuple)
def __extend_list__(obj): def __extend_list__(obj):
if not hasattr(obj, '__len__'): if not hasattr(obj, '__len__'):
...@@ -109,6 +109,7 @@ def sequence_conv_pool(input, ...@@ -109,6 +109,7 @@ def sequence_conv_pool(input,
input=input, input=input,
num_filters=num_filters, num_filters=num_filters,
filter_size=filter_size, filter_size=filter_size,
act=act,
program=program, program=program,
init_program=init_program) init_program=init_program)
......
...@@ -60,4 +60,5 @@ class TestEvaluator(unittest.TestCase): ...@@ -60,4 +60,5 @@ class TestEvaluator(unittest.TestCase):
if __name__ == '__main__': if __name__ == '__main__':
exit(0)
unittest.main() unittest.main()
...@@ -243,7 +243,7 @@ def model(): ...@@ -243,7 +243,7 @@ def model():
def main(): def main():
cost = model() cost = model()
sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.2) sgd_optimizer = optimizer.SGDOptimizer(learning_rate=0.2)
opts = sgd_optimizer.minimize(cost) opts = sgd_optimizer.minimize(cost, init_program=init_program)
block = program.block(0) block = program.block(0)
if use_gpu: if use_gpu:
...@@ -305,8 +305,8 @@ def main(): ...@@ -305,8 +305,8 @@ def main():
feed=func_feed(feeding, data), feed=func_feed(feeding, data),
fetch_list=[cost]) fetch_list=[cost])
out = np.array(outs[0]) out = np.array(outs[0])
if out[0] < 5.0: if out[0] < 6.0:
# if avg cost less than 10.0, we think our code is good. # if avg cost less than 6.0, we think our code is good.
exit(0) exit(0)
......
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