complete python reader op python side

paddle/fluid/pybind/pybind.cc

@@ -449,6 +449,8 @@ All parameter, weight, gradient are variables in Paddle.
       });
 
   py::class_<LoDTensorArray>(m, "LoDTensorArray")
+      .def("__init__",
+           [](LoDTensorArray &instance) { new (&instance) LoDTensorArray(); })
       .def("__getitem__",
            [](LoDTensorArray &self, size_t i) { return &self.at(i); },
            py::return_value_policy::reference)

python/paddle/fluid/__init__.py

@@ -44,7 +44,7 @@ import metrics
 import transpiler
 from param_attr import ParamAttr, WeightNormParamAttr
 from data_feeder import DataFeeder
-from core import LoDTensor, CPUPlace, CUDAPlace, CUDAPinnedPlace
+from core import LoDTensor, LoDTensorArray, CPUPlace, CUDAPlace, CUDAPinnedPlace, Scope
 from transpiler import DistributeTranspiler, InferenceTranspiler, \
     memory_optimize, release_memory
 from concurrency import (Go, make_channel, channel_send, channel_recv,
@@ -72,6 +72,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + \
               'backward',
               'regularizer',
               'LoDTensor',
+              'LoDTensorArray',
               'CPUPlace',
               'CUDAPlace',
               'CUDAPinnedPlace',

python/paddle/fluid/layers/io.py

@@ -22,9 +22,10 @@ from ..executor import global_scope
 from layer_function_generator import generate_layer_fn, templatedoc
 
 __all__ = [
-    'data', 'BlockGuardServ', 'ListenAndServ', 'Send', 'open_recordio_file',
-    'open_files', 'read_file', 'shuffle', 'batch', 'double_buffer',
-    'random_data_generator', 'Preprocessor', 'load'
+    'data', 'BlockGuardServ', 'ListenAndServ', 'Send', 'Recv',
+    'open_recordio_file', 'open_files', 'read_file', 'shuffle', 'batch',
+    'double_buffer', 'random_data_generator', 'py_reader', 'Preprocessor',
+    'load'
 ]
 
 
@@ -431,6 +432,88 @@ def random_data_generator(low, high, shapes, lod_levels, for_parallel=True):
     return monkey_patch_reader_methods(main_prog_var)
 
 
+def py_reader(capacity, shapes, lod_levels, dtypes, for_parallel=True):
+    """
+    Create a reader and blocking queue for data feeding in Python
+
+    This layer returns a Reader Variable and a BlockingQueue.
+	The BlockingQueue provides `push()` method to push a 
+	`LoDTensorArray` object into the queue in Python side. In C++
+	side, the Reader Variable would invoke `pop()` method of the
+	queue to retrieve the feeding data. The process of feeding data
+	in Python side and fetching data in C++ side can run in parallel.
+	The BlockingQueue should be closed using `close()` method when
+	unused.
+
+    Args:
+       capacity(int): The maximum capacity of the BlockingQueue.
+       shapes(list): List of tuples which declaring data shapes.
+       lod_levels(list): List of ints which declaring data lod_level.
+	   dtypes(list): List of strs which declaring data type.
+       for_parallel(Bool): Set it as True if you are going to run
+            subsequent operators in parallel.
+
+    Returns:
+       Variable: A Reader Variable from which we can get feeding data.
+	   BlockingQueue: A blocking queue for data feeding.
+
+    Examples:
+
+        .. code-block:: python
+
+            reader, queue = fluid.layers.py_reader(
+                                             capacity=10,
+                                             shapes=[[-1,3,224,224], [-1,1]],
+                                             lod_levels=[0, 0],
+                                             dtypes=['float32', 'int64'])
+            # Via the reader, we can use 'read_file' layer to get data:
+            image, label = fluid.layers.read_file(reader)
+            # Via the blocking queue, we can feed data using threads
+            def feed_data(queue, feed_images, feed_labels):
+                for feed_image, feed_label in zip(feed_images, feed_labels):
+                    data = core.LoDTensorArray()
+                    data.append(feed_image)
+                    data.append(feed_label)
+                    queue.push(data)
+            
+            thread = threading.Thread(target=feed_data, args=(queue, feed_images, feed_labels))
+    """
+    dtypes = [convert_np_dtype_to_dtype_(dt) for dt in dtypes]
+    shape_concat = []
+    ranks = []
+
+    for shape in shapes:
+        shape_concat.extend(shape)
+        ranks.append(len(shape))
+
+    queue_name = unique_name('lod_tensor_blocking_queue')
+    var = global_scope().var(queue_name)
+    feed_queue = core.init_lod_tensor_blocking_queue(var, capacity, shapes)
+
+    startup_blk = default_startup_program().current_block()
+    startup_var = startup_blk.create_var(name=unique_name('create_py_reader'))
+    startup_blk.append_op(
+        type='create_py_reader',
+        inputs={'blocking_queue': queue_name},
+        outputs={'Out': [startup_var]},
+        attrs={
+            'shape_concat': shape_concat,
+            'lod_levels': lod_levels,
+            'ranks': ranks
+        })
+
+    startup_var.desc.set_dtypes(dtypes)
+    startup_var.persistable = True
+
+    main_prog_var = _copy_reader_var_(default_main_program().current_block(),
+                                      startup_var)
+
+    if for_parallel:
+        main_prog_var = parallel(reader=main_prog_var)
+
+    return monkey_patch_reader_methods(main_prog_var), feed_queue
+
+
 def open_files(filenames,
                shapes,
                lod_levels,

python/paddle/fluid/tests/unittests/test_py_reader_push_pop.py

+# Copyright (c) 2018 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 unittest
+import paddle.fluid as fluid
+import numpy as np
+from threading import Thread
+
+
+def feed_data(feed_queue, inputs):
+    for in_data in inputs:
+        feed_queue.push(in_data)
+
+
+class TestPyReader(unittest.TestCase):
+    def setUp(self):
+        self.capacity = 10
+        self.batch_size_min = 10
+        self.batch_size_max = 20
+        self.shapes = [(-1, 3, 2, 1), (-1, 1)]
+        self.lod_levels = [0, 0]
+        self.dtypes = ['float32', 'int64']
+        self.iterations = 20
+
+    def test_single_thread_main(self):
+        self.main(use_thread=False)
+
+    def test_multiple_thread_main(self):
+        self.main(use_thread=True)
+
+    def main(self, use_thread=False):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+            ) else fluid.CPUPlace()
+            executor = fluid.Executor(place)
+
+            data_file, feed_queue = fluid.layers.py_reader(
+                capacity=self.capacity,
+                dtypes=self.dtypes,
+                lod_levels=self.lod_levels,
+                shapes=self.shapes)
+
+            read_out_data = fluid.layers.read_file(data_file)
+            self.inputs = []
+
+            for i in range(self.iterations):
+                in_data = fluid.LoDTensorArray()
+                batch_size = np.random.random_integers(self.batch_size_min,
+                                                       self.batch_size_max)
+                for shape, dtype in zip(self.shapes, self.dtypes):
+                    next_data = np.random.uniform(
+                        low=0, high=1000,
+                        size=(batch_size, ) + shape[1:]).astype(dtype)
+                    in_data.append(executor.as_lodtensor(next_data))
+
+                self.inputs.append(in_data)
+
+            executor.run(fluid.default_startup_program())
+            self.outputs = []
+            if use_thread:
+                thread = Thread(
+                    target=feed_data, args=(feed_queue, self.inputs))
+                thread.start()
+                for in_data in self.inputs:
+                    self.outputs.append(
+                        executor.run(fetch_list=list(read_out_data)))
+            else:
+                for in_data in self.inputs:
+                    feed_queue.push(in_data)
+                    self.outputs.append(
+                        executor.run(fetch_list=list(read_out_data)))
+
+            feed_queue.close()
+            self.validate()
+
+    def validate(self):
+        self.assertEqual(len(self.inputs), len(self.outputs))
+        for in_data_list, out_data_list in zip(self.inputs, self.outputs):
+            self.assertEqual(len(in_data_list), len(out_data_list))
+            in_data_list_np = [
+                np.array(in_lod_tensor) for in_lod_tensor in in_data_list
+            ]
+            for in_data, out_data in zip(in_data_list_np, out_data_list):
+                self.assertTrue((in_data == out_data).all())
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_py_reader_using_executor.py

+# Copyright (c) 2018 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 unittest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import numpy as np
+import threading
+import multiprocessing
+import os
+
+
+def as_tensor(np_array_or_tensor, place=None):
+    if isinstance(np_array_or_tensor, fluid.LoDTensor):
+        return np_array_or_tensor
+
+    if place is None:
+        place = fluid.CPUPlace()
+
+    tensor = fluid.LoDTensor()
+    tensor.set(np_array_or_tensor, place)
+    return tensor
+
+
+def as_numpy(tensor_or_numpy):
+    return tensor_or_numpy if isinstance(
+        tensor_or_numpy, np.ndarray) else np.array(tensor_or_numpy)
+
+
+def feed_data(feed_queue, reader):
+    data_generator = reader()
+    while True:
+        data = next(data_generator, None)
+        if data is None or not feed_queue.push(data):
+            break
+
+
+def simple_fc_net(in_size,
+                  class_num,
+                  hidden_sizes,
+                  batch_size,
+                  queue_capacity,
+                  use_double_buffer=False):
+    reader, feed_queue = fluid.layers.py_reader(
+        capacity=queue_capacity,
+        shapes=[[-1, in_size], [-1, 1]],
+        lod_levels=[0, 0],
+        dtypes=['float32', 'int64'])
+    reader = fluid.layers.batch(reader, batch_size=batch_size)
+    if use_double_buffer:
+        reader = fluid.layers.double_buffer(reader)
+
+    in_data, label = fluid.layers.read_file(reader)
+
+    hidden = in_data
+    for hidden_size in hidden_sizes:
+        hidden = fluid.layers.fc(
+            hidden,
+            size=hidden_size,
+            act='tanh',
+            bias_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=1.0)))
+
+    predict_label = fluid.layers.fc(hidden, size=class_num, act='softmax')
+    loss = fluid.layers.mean(
+        fluid.layers.cross_entropy(
+            input=predict_label, label=label))
+
+    optimizer = fluid.optimizer.Adam()
+    optimizer.minimize(loss)
+    return in_data, label, loss, optimizer, feed_queue
+
+
+class TestPyReaderUsingExecutor(unittest.TestCase):
+    def setUp(self):
+        self.in_size = 1000
+        self.hidden_sizes = [50, 30, 20]
+        self.class_num = 10
+        self.batch_size = 32
+        self.iterations = 10
+        self.queue_capacity = 50
+
+    def test(self):
+        for use_cuda in [False, True]:
+            for use_parallel_executor in [False, True]:
+                for use_double_buffer in [False, True]:
+                    print('Test Parameters:'),
+                    print({
+                        'use_cuda': use_cuda,
+                        'use_parallel_executor': use_parallel_executor,
+                        'use_double_buffer': use_double_buffer
+                    })
+                    self.main(use_cuda, use_parallel_executor,
+                              use_double_buffer)
+
+    def random_reader(self):
+        def reader():
+            self.inputs = []
+            cnt = 0
+            while True:
+                tensors = fluid.LoDTensorArray()
+                in_data = np.random.uniform(
+                    low=0, high=1, size=(1, self.in_size)).astype('float32')
+                tensors.append(as_tensor(in_data))
+                label = np.random.random_integers(
+                    low=0, high=self.class_num - 1, size=(1, 1)).astype('int64')
+                tensors.append(as_tensor(label))
+
+                if cnt < self.iterations * self.batch_size * self.batch_size_times:
+                    if cnt % (self.batch_size * self.batch_size_times) == 0:
+                        self.inputs.append([in_data, label])
+                    else:
+                        self.inputs[-1][0] = np.concatenate(
+                            (self.inputs[-1][0], in_data), axis=0)
+                        self.inputs[-1][1] = np.concatenate(
+                            (self.inputs[-1][1], label), axis=0)
+                elif not self.use_double_buffer:
+                    break
+
+                yield tensors
+                cnt += 1
+
+            yield None
+
+        return reader
+
+    def main(self,
+             use_cuda=True,
+             use_parallel_executor=False,
+             use_double_buffer=False):
+        assert not use_cuda or use_cuda and core.is_compiled_with_cuda()
+
+        self.use_cuda = use_cuda
+        self.use_parallel_executor = use_parallel_executor
+        self.use_double_buffer = use_double_buffer
+
+        startup_program = fluid.Program()
+        main_program = fluid.Program()
+
+        with fluid.program_guard(main_program, startup_program):
+            in_data, label, loss, optimizer, feed_queue = simple_fc_net(
+                in_size=self.in_size,
+                class_num=self.class_num,
+                hidden_sizes=self.hidden_sizes,
+                batch_size=self.batch_size,
+                queue_capacity=self.queue_capacity,
+                use_double_buffer=self.use_double_buffer)
+
+            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+
+            startup_exe = fluid.Executor(place)
+            startup_exe.run(startup_program)
+
+            if use_parallel_executor:
+                main_exe = fluid.ParallelExecutor(use_cuda, loss_name=loss.name)
+                if use_cuda:
+                    self.batch_size_times = core.get_cuda_device_count()
+                else:
+                    self.batch_size_times = int(
+                        os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
+            else:
+                main_exe = startup_exe
+                self.batch_size_times = 1
+
+            reader = self.random_reader()
+            thread = threading.Thread(
+                target=feed_data, args=(feed_queue, reader))
+            thread.start()
+
+            self.outputs = []
+            for _ in range(self.iterations):
+                fetches = main_exe.run(fetch_list=[in_data.name, label.name])
+                fetches = [as_numpy(fetch) for fetch in fetches]
+                self.outputs.append(fetches)
+
+            self.validate()
+            feed_queue.close()
+
+    def validate(self):
+        self.assertEqual(len(self.inputs), len(self.outputs))
+        for batch_in, batch_out in zip(self.inputs, self.outputs):
+            self.assertEqual(len(batch_in), len(batch_out))
+            if self.use_parallel_executor and not self.use_double_buffer:
+                self.validate_unordered_batch(batch_in, batch_out)
+            else:
+                for in_data, out_data in zip(batch_in, batch_out):
+                    self.assertEqual(in_data.shape, out_data.shape)
+                    if not self.use_parallel_executor:
+                        self.assertTrue((in_data == out_data).all())
+
+    def validate_unordered_batch(self, batch_in, batch_out):
+        out_index_left_set = set(range(self.batch_size * self.batch_size_times))
+        mapping_num = 0
+        for i in range(self.batch_size * self.batch_size_times):
+            for j in out_index_left_set:
+                flag = True
+                for k in range(len(batch_in)):
+                    in_data = batch_in[k][i]
+                    out_data = batch_out[k][j]
+                    if (in_data != out_data).any():
+                        flag = False
+                        break
+
+                if flag:
+                    out_index_left_set.remove(j)
+                    mapping_num += 1
+                    break
+
+        self.assertEqual(mapping_num, self.batch_size * self.batch_size_times)
+
+
+if __name__ == '__main__':
+    unittest.main()