Merge pull request #736 from kuke/decoder_wrapper

Add pybind11 wrapper for decoder

fluid/DeepASR/data_utils/util.py

@@ -50,6 +50,13 @@ def batch_to_ndarray(batch_samples, lod):
     return (batch_feature, batch_label)
 
 
+def split_infer_result(infer_seq, lod):
+    infer_batch = []
+    for i in xrange(0, len(lod[0]) - 1):
+        infer_batch.append(infer_seq[lod[0][i]:lod[0][i + 1]])
+    return infer_batch
+
+
 class DaemonProcessGroup(object):
     def __init__(self, proc_num, target, args):
         self._proc_num = proc_num

fluid/DeepASR/decoder/decoder.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include "decoder.h"
+
+std::string decode(std::vector<std::vector<float>> probs_mat) {
+  // Add decoding logic here
+
+  return "example decoding result";
+}

fluid/DeepASR/decoder/decoder.h

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <string>
+#include <vector>
+
+std::string decode(std::vector<std::vector<float>> probs_mat);

fluid/DeepASR/decoder/pybind.cc

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+
+#include "decoder.h"
+
+namespace py = pybind11;
+
+PYBIND11_MODULE(decoder, m) {
+  m.doc() = "Decode function for Deep ASR model";
+
+  m.def("decode",
+        &decode,
+        "Decode one input probability matrix "
+        "and return the transcription");
+}

fluid/DeepASR/decoder/setup.py

+#  Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import os
+from distutils.core import setup, Extension
+from distutils.sysconfig import get_config_vars
+
+args = ['-std=c++11']
+
+# remove warning about -Wstrict-prototypes
+(opt, ) = get_config_vars('OPT')
+os.environ['OPT'] = " ".join(flag for flag in opt.split()
+                             if flag != '-Wstrict-prototypes')
+
+ext_modules = [
+    Extension(
+        'decoder',
+        ['pybind.cc', 'decoder.cc'],
+        include_dirs=['pybind11/include', '.'],
+        language='c++',
+        extra_compile_args=args, ),
+]
+
+setup(
+    name='decoder',
+    version='0.0.1',
+    author='Paddle',
+    author_email='',
+    description='Decoder for Deep ASR model',
+    ext_modules=ext_modules, )

fluid/DeepASR/decoder/setup.sh

+
+
+if [ ! -d pybind11 ]; then
+    git clone https://github.com/pybind/pybind11.git
+fi 
+
+python setup.py build_ext -i 

fluid/DeepASR/infer.py

@@ -10,6 +10,7 @@ import data_utils.augmentor.trans_add_delta as trans_add_delta
 import data_utils.augmentor.trans_splice as trans_splice
 import data_utils.data_reader as reader
 from data_utils.util import lodtensor_to_ndarray
+from data_utils.util import split_infer_result
 
 
 def parse_args():
@@ -58,13 +59,6 @@ def print_arguments(args):
     print('------------------------------------------------')
 
 
-def split_infer_result(infer_seq, lod):
-    infer_batch = []
-    for i in xrange(0, len(lod[0]) - 1):
-        infer_batch.append(infer_seq[lod[0][i]:lod[0][i + 1]])
-    return infer_batch
-
-
 def infer(args):
     """ Gets one batch of feature data and predicts labels for each sample.
     """

fluid/DeepASR/infer_by_ckpt.py

@@ -13,8 +13,10 @@ import data_utils.augmentor.trans_mean_variance_norm as trans_mean_variance_norm
 import data_utils.augmentor.trans_add_delta as trans_add_delta
 import data_utils.augmentor.trans_splice as trans_splice
 import data_utils.async_data_reader as reader
+import decoder.decoder as decoder
 from data_utils.util import lodtensor_to_ndarray
 from model_utils.model import stacked_lstmp_model
+from data_utils.util import split_infer_result
 
 
 def parse_args():
@@ -141,13 +143,20 @@ def infer_from_ckpt(args):
 
         infer_data_reader.recycle(features, labels, lod)
 
-        cost, acc = exe.run(infer_program,
-                            feed={"feature": feature_t,
-                                  "label": label_t},
-                            fetch_list=[avg_cost, accuracy],
-                            return_numpy=False)
-        infer_costs.append(lodtensor_to_ndarray(cost)[0])
-        infer_accs.append(lodtensor_to_ndarray(acc)[0])
+        results = exe.run(infer_program,
+                          feed={"feature": feature_t,
+                                "label": label_t},
+                          fetch_list=[prediction, avg_cost, accuracy],
+                          return_numpy=False)
+        infer_costs.append(lodtensor_to_ndarray(results[1])[0])
+        infer_accs.append(lodtensor_to_ndarray(results[2])[0])
+
+        probs, lod = lodtensor_to_ndarray(results[0])
+        infer_batch = split_infer_result(probs, lod)
+        for index, sample in enumerate(infer_batch):
+            print("Decoding %d: " % (batch_id * args.batch_size + index),
+                  decoder.decode(sample))
+
     print(np.mean(infer_costs), np.mean(infer_accs))