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fix sequence_project_op forward and backward

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revert-4814-Add_sequence_project_op
chengduoZH 8 years ago
parent 40688d223e
commit 834b82f109
3 changed files with 292 additions and 126 deletions
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28
paddle/operators/sequence_project_op.cc
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@ -38,24 +38,23 @@ class SequenceProjectOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(
ctx->HasInput("PaddingData"),
"Output(PaddingData) of SequenceProjectOp should not be null.");
framework::DDim padding_dim = ctx->GetOutputDim("PaddingData");
framework::DDim padding_dim = ctx->GetInputDim("PaddingData");
int up_pad = std::max(0, -context_start);
int down_pad = std::max(0, context_start + context_length - 1);
int total_pad = up_pad + down_pad;
int input_width = static_cast<int>(in_dims[1]);
if (context_start == 0 && context_length == 1) {
PADDLE_THROW(
"if context_start == 0 && context_length == 1, padding_trainable "
"should be false.");
}
PADDLE_ENFORCE(padding_dim.size() == 2,
"Input(PaddingData) should be 2-D tensor.");
PADDLE_ENFORCE(
padding_dim[0] == total_pad && padding_dim[1] == input_width,
"Input(PaddingData)'s shape is not consistent with 'context_start' "
"and 'context_length'.");
if (context_start == 0 && context_length == 1) {
PADDLE_THROW(
"if context_start == 0 && context_length == 1, padding_trainable "
"should be false.");
}
}
in_dims[1] = in_dims[1] * context_length;
@ -74,9 +73,11 @@ class SequenceProjectGradOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null.");
if (ctx->Attrs().Get<bool>("padding_trainable")) {
PADDLE_ENFORCE(
ctx->HasOutput("PaddingData"),
"Output(PaddingData) of SequenceProjectOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("PaddingData")),
"Output(PaddingData@GRAD) of SequenceProjectGradOp should "
"not be null.");
auto padding_dims = ctx->GetInputDim("PaddingData");
ctx->SetOutputDim(framework::GradVarName("PaddingData"), padding_dims);
}
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
}
@ -93,8 +94,8 @@ class SequenceProjectOpMaker : public framework::OpProtoAndCheckerMaker {
AddOutput(
"Out",
"A float LoDTensor, the variable-length output of SequenceProjectOp.");
AddOutput("PaddingData",
"A float LoDTensor, the padding data of SequenceProjectOp.");
AddInput("PaddingData", // PaddingData can be a float tensor
"A float LoDTensor, the padding data of SequenceProjectOp.");
AddAttr<bool>("padding_trainable",
"(bool, default false) the padding data of SequenceProjectOp "
@ -110,7 +111,8 @@ class SequenceProjectOpMaker : public framework::OpProtoAndCheckerMaker {
AddAttr<int>("context_stride",
"(int, default 1) the xx of SequenceProjectOp.")
.SetDefault(1)
.GreaterThan(0);
.GreaterThan(
0); // Currently, sequence_project_op only support context_stride=1
AddComment(R"DOC(
SequenceProjectOp projects features of context_length time-steps of each instance.

267
paddle/operators/sequence_project_op.h
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123
python/paddle/v2/framework/tests/test_seq_project.py
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@ -1,5 +1,6 @@
import unittest
import numpy as np
import random
from op_test import OpTest
@ -10,18 +11,22 @@ class TestSeqProject(OpTest):
# one level, batch size
x = np.random.uniform(
0.1, 1, [self.input_size[0], self.input_size[1]]).astype('float32')
lod = [[0, 4, 5, 8, self.input_size[0]]]
self.begin_pad = np.max([0, -self.context_start])
self.end_pad = np.max([0, self.context_start + self.context_length - 1])
self.total_pad = self.begin_pad + self.end_pad
w = np.ones((self.total_pad, self.input_size[1])) * 100
self.inputs = {'X': (x, lod), 'PaddingData': w}
# w = np.ones((self.total_pad, self.input_size[1])) * 100
w = np.array(range(self.total_pad * self.input_size[1]))
w.shape = self.total_pad, self.input_size[1]
self.inputs = {
'X': (x, self.lod),
'PaddingData': (w, [[0, self.total_pad]])
}
self.attrs = {
'context_start': self.context_start,
'context_length': self.context_length,
'padding_trainable': self.padding_trainable
'padding_trainable': self.padding_trainable,
'context_stride': self.context_stride
}
out = np.zeros((self.input_size[0], self.input_size[1] *
self.context_length)).astype('float32')
@ -30,9 +35,10 @@ class TestSeqProject(OpTest):
def compute(self):
x, lod = self.inputs['X']
w = self.inputs['PaddingData']
w, _ = self.inputs['PaddingData']
out = self.outputs['Out']
lod = lod[0]
begin_pad = np.max([0, -self.context_start])
for i in range(len(lod) - 1):
for j in range(self.context_length):
@ -43,22 +49,20 @@ class TestSeqProject(OpTest):
if in_begin < lod[i]:
pad_size = np.min([lod[i] - in_begin, lod[i + 1] - lod[i]])
if self.padding_trainable:
sub_w = w[j:pad_size, :]
sub_w = w[j:j + pad_size, :]
out[lod[i]:lod[i] + pad_size, j * self.input_size[1]:(
j + 1) * self.input_size[1]] = sub_w
# pass
out_begin = lod[i] + pad_size
in_begin = lod[i]
if in_end > lod[i + 1]:
pad_size = np.min(
[in_end - lod[i + 1], lod[i + 1] - lod[i]])
out_sub = out[lod[i + 1] - pad_size:lod[i + 1], :]
if self.padding_trainable:
sub_w = w[j - pad_size:j, :]
sub_w = w[begin_pad + self.context_start + j - pad_size:
begin_pad + self.context_start + j, :]
out[lod[i + 1] - pad_size:lod[i + 1], j * self.
input_size[1]:(j + 1) * self.input_size[1]] = sub_w
# pass
in_end = lod[i + 1]
out_end = lod[i + 1] - pad_size
if in_end <= in_begin:
@ -69,28 +73,105 @@ class TestSeqProject(OpTest):
self.input_size[1]] += in_sub
def init_test_case(self):
self.input_size = [11, 23]
self.input_row = 11
self.input_size = [self.input_row, 23]
self.lod = [[0, 4, 5, 8, self.input_row]]
self.op_type = "sequence_project"
self.context_start = -1
self.context_length = 3
self.padding_trainable = False
self.padding_trainable = True
self.context_stride = 1
def test_check_output(self):
self.check_output()
# def test_check_grad(self):
# self.check_grad(["X"], "Out")
# self.check_grad(
# set(['X', 'PaddingData']), 'Out', max_relative_error=0.05)
# class TestSeqAvgPool2D(TestSeqProject):
# def init_test_case(self):
# self.input_size = [11, 23]
# self.op_type = "sequence_project"
# def test_check_grad_no_filter(self):
# self.check_grad(
# ['X'],
# 'Out',
# max_relative_error=0.05,
# no_grad_set=set(['PaddingData']))
#
# self.context_start = -1
# self.context_length = 3
# self.padding_trainable = True
# def test_check_grad_no_input(self):
# self.check_grad(
# ['PaddingData'],
# 'Out',
# max_relative_error=0.05,
# no_grad_set=set(['X']))
'''
class TestSeqProjectCases(TestSeqProject):
def setUp(self):
self.init_test_case()
self.op_type = 'sequence_project'
num = 0
for context_start in [-5, -3, -1, 0, 3]:
for context_length in [1, 2, 5, 7]:
for batch_size in [1, 2, 5, 7]:
for padding_trainable in [False, True]:
if context_length == 1 and context_start == 0 and padding_trainable:
continue
self.context_start = context_start
self.context_length = context_length
self.padding_trainable = padding_trainable
self.input_size = [batch_size, 23]
x = np.random.uniform(0.1, 1,
self.input_size).astype('float32')
self.lod = [[0, self.input_size[0]]]
if self.input_size[0] > 2:
idx = range(self.input_size[0])
del idx[0]
self.lod = [
[0] + np.sort(random.sample(idx, 2)).tolist() +
[self.input_size[0]]
]
self.begin_pad = np.max([0, -self.context_start])
self.end_pad = np.max(
[0, self.context_start + self.context_length - 1])
self.total_pad = self.begin_pad + self.end_pad
# w = np.ones((self.total_pad, self.input_size[1])) * 100
w = np.array(range(self.total_pad * self.input_size[1]))
w.shape = self.total_pad, self.input_size[1]
if self.total_pad * self.input_size[1] == 0:
w = np.random.uniform(
0.1, 1,
(1, self.input_size[1])).astype('float32')
self.total_pad = 1
self.inputs = {
'X': (x, self.lod),
'PaddingData': (w, [[0, self.total_pad]])
}
self.attrs = {
'context_start': self.context_start,
'context_length': self.context_length,
'padding_trainable': self.padding_trainable,
'context_stride': self.context_stride
}
out = np.zeros((self.input_size[0], self.input_size[1] *
self.context_length)).astype('float32')
self.outputs = {'Out': out}
print num
print self.attrs
print batch_size
print padding_trainable
print "$$$$$$$$$$$$$"
self.compute()
self.test_check_output()
num += 1
'''
if __name__ == '__main__':
unittest.main()

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