Image classification & word2vec (#10738)

python/paddle/fluid/tests/book/high-level-api/CMakeLists.txt

@@ -8,3 +8,4 @@ endforeach()
 
 add_subdirectory(fit_a_line)
 add_subdirectory(recognize_digits)
+add_subdirectory(image_classification)

python/paddle/fluid/tests/book/high-level-api/image_classification/CMakeLists.txt

+file(GLOB TEST_OPS RELATIVE "${CMAKE_CURRENT_SOURCE_DIR}" "test_*.py")
+string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}")
+
+# default test
+foreach(src ${TEST_OPS})
+    py_test(${src} SRCS ${src}.py)
+endforeach()

python/paddle/fluid/tests/book/high-level-api/image_classification/cifar10_small_test_set.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.
+"""
+CIFAR dataset.
+
+This module will download dataset from
+https://www.cs.toronto.edu/~kriz/cifar.html and parse train/test set into
+paddle reader creators.
+
+The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes,
+with 6000 images per class. There are 50000 training images and 10000 test
+images.
+
+The CIFAR-100 dataset is just like the CIFAR-10, except it has 100 classes
+containing 600 images each. There are 500 training images and 100 testing
+images per class.
+
+"""
+
+import cPickle
+import itertools
+import numpy
+import paddle.v2.dataset.common
+import tarfile
+
+__all__ = ['train10']
+
+URL_PREFIX = 'https://www.cs.toronto.edu/~kriz/'
+CIFAR10_URL = URL_PREFIX + 'cifar-10-python.tar.gz'
+CIFAR10_MD5 = 'c58f30108f718f92721af3b95e74349a'
+
+
+def reader_creator(filename, sub_name, batch_size=None):
+    def read_batch(batch):
+        data = batch['data']
+        labels = batch.get('labels', batch.get('fine_labels', None))
+        assert labels is not None
+        for sample, label in itertools.izip(data, labels):
+            yield (sample / 255.0).astype(numpy.float32), int(label)
+
+    def reader():
+        with tarfile.open(filename, mode='r') as f:
+            names = (each_item.name for each_item in f
+                     if sub_name in each_item.name)
+
+            batch_count = 0
+            for name in names:
+                batch = cPickle.load(f.extractfile(name))
+                for item in read_batch(batch):
+                    if isinstance(batch_size, int) and batch_count > batch_size:
+                        break
+                    batch_count += 1
+                    yield item
+
+    return reader
+
+
+def train10(batch_size=None):
+    """
+    CIFAR-10 training set creator.
+
+    It returns a reader creator, each sample in the reader is image pixels in
+    [0, 1] and label in [0, 9].
+
+    :return: Training reader creator
+    :rtype: callable
+    """
+    return reader_creator(
+        paddle.v2.dataset.common.download(CIFAR10_URL, 'cifar', CIFAR10_MD5),
+        'data_batch',
+        batch_size=batch_size)

python/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_resnet.py → python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_resnet.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import numpy
+import cifar10_small_test_set
 
 
 def resnet_cifar10(input, depth=32):
@@ -81,46 +82,50 @@ def train_network():
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=predict, label=label)
-    return avg_cost, accuracy
+    return [avg_cost, accuracy]
 
 
-def train(use_cuda, save_path):
+def train(use_cuda, train_program, save_dirname):
     BATCH_SIZE = 128
     EPOCH_NUM = 1
 
     train_reader = paddle.batch(
         paddle.reader.shuffle(
-            paddle.dataset.cifar.train10(), buf_size=128 * 10),
+            cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
         batch_size=BATCH_SIZE)
 
     test_reader = paddle.batch(
         paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
 
     def event_handler(event):
-        if isinstance(event, fluid.EndIteration):
-            if (event.batch_id % 10) == 0:
-                avg_cost, accuracy = trainer.test(reader=test_reader)
+        if isinstance(event, fluid.EndStepEvent):
+            avg_cost, accuracy = trainer.test(
+                reader=test_reader, feed_order=['pixel', 'label'])
 
-                print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
-                    event.batch_id + 1, avg_cost, accuracy))
+            print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
 
             if accuracy > 0.01:  # Low threshold for speeding up CI
-                    trainer.params.save(save_path)
+                if save_dirname is not None:
+                    trainer.save_params(save_dirname)
                 return
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     trainer = fluid.Trainer(
-        train_network,
+        train_func=train_program,
         optimizer=fluid.optimizer.Adam(learning_rate=0.001),
-        place=place,
-        event_handler=event_handler)
-    trainer.train(train_reader, EPOCH_NUM, event_handler=event_handler)
+        place=place)
 
+    trainer.train(
+        reader=train_reader,
+        num_epochs=EPOCH_NUM,
+        event_handler=event_handler,
+        feed_order=['pixel', 'label'])
 
-def infer(use_cuda, save_path):
-    params = fluid.Params(save_path)
+
+def infer(use_cuda, inference_program, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.Inferencer(inference_network, params, place=place)
+    inferencer = fluid.Inferencer(
+        infer_func=inference_program, param_path=save_dirname, place=place)
 
     # The input's dimension of conv should be 4-D or 5-D.
     # Use normilized image pixels as input data, which should be in the range
@@ -135,8 +140,14 @@ def main(use_cuda):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
     save_path = "image_classification_resnet.inference.model"
-    train(use_cuda, save_path)
-    infer(use_cuda, save_path)
+
+    train(
+        use_cuda=use_cuda, train_program=train_network, save_dirname=save_path)
+
+    infer(
+        use_cuda=use_cuda,
+        inference_program=inference_network,
+        save_dirname=save_path)
 
 
 if __name__ == '__main__':

python/paddle/fluid/tests/book/high-level-api/image_classification/notest_image_classification_vgg.py → python/paddle/fluid/tests/book/high-level-api/image_classification/test_image_classification_vgg.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 import numpy
+import cifar10_small_test_set
 
 
 def vgg16_bn_drop(input):
@@ -60,46 +61,48 @@ def train_network():
     cost = fluid.layers.cross_entropy(input=predict, label=label)
     avg_cost = fluid.layers.mean(cost)
     accuracy = fluid.layers.accuracy(input=predict, label=label)
-    return avg_cost, accuracy
+    return [avg_cost, accuracy]
 
 
-def train(use_cuda, save_path):
+def train(use_cuda, train_program, save_dirname):
     BATCH_SIZE = 128
-    EPOCH_NUM = 1
-
     train_reader = paddle.batch(
         paddle.reader.shuffle(
-            paddle.dataset.cifar.train10(), buf_size=128 * 10),
+            cifar10_small_test_set.train10(batch_size=10), buf_size=128 * 10),
         batch_size=BATCH_SIZE)
 
     test_reader = paddle.batch(
         paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE)
 
     def event_handler(event):
-        if isinstance(event, fluid.EndIteration):
-            if (event.batch_id % 10) == 0:
-                avg_cost, accuracy = trainer.test(reader=test_reader)
+        if isinstance(event, fluid.EndStepEvent):
+            avg_cost, accuracy = trainer.test(
+                reader=test_reader, feed_order=['pixel', 'label'])
 
-                print('BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'.format(
-                    event.batch_id + 1, avg_cost, accuracy))
+            print('Loss {0:2.2}, Acc {1:2.2}'.format(avg_cost, accuracy))
 
             if accuracy > 0.01:  # Low threshold for speeding up CI
-                    trainer.params.save(save_path)
+                if save_dirname is not None:
+                    trainer.save_params(save_dirname)
                 return
 
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
     trainer = fluid.Trainer(
-        train_network,
-        optimizer=fluid.optimizer.Adam(learning_rate=0.001),
+        train_func=train_program,
         place=place,
-        event_handler=event_handler)
-    trainer.train(train_reader, EPOCH_NUM, event_handler=event_handler)
+        optimizer=fluid.optimizer.Adam(learning_rate=0.001))
+
+    trainer.train(
+        reader=train_reader,
+        num_epochs=1,
+        event_handler=event_handler,
+        feed_order=['pixel', 'label'])
 
 
-def infer(use_cuda, save_path):
-    params = fluid.Params(save_path)
+def infer(use_cuda, inference_program, save_dirname=None):
     place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
-    inferencer = fluid.Inferencer(inference_network, params, place=place)
+    inferencer = fluid.Inferencer(
+        infer_func=inference_program, param_path=save_dirname, place=place)
 
     # The input's dimension of conv should be 4-D or 5-D.
     # Use normilized image pixels as input data, which should be in the range
@@ -114,8 +117,14 @@ def main(use_cuda):
     if use_cuda and not fluid.core.is_compiled_with_cuda():
         return
     save_path = "image_classification_vgg.inference.model"
-    train(use_cuda, save_path)
-    infer(use_cuda, save_path)
+
+    train(
+        use_cuda=use_cuda, train_program=train_network, save_dirname=save_path)
+
+    infer(
+        use_cuda=use_cuda,
+        inference_program=inference_network,
+        save_dirname=save_path)
 
 
 if __name__ == '__main__':