Merge branch 'master' into new_snapshot

chainer/__init__.py

@@ -7,26 +7,16 @@
 
 from chainer import _version
 from chainer import backends  # NOQA
-from chainer import configuration  # NOQA
 from chainer import dataset  # NOQA
 from chainer import datasets  # NOQA
-from chainer import function  # NOQA
-from chainer import function_hook  # NOQA
 from chainer import function_hooks  # NOQA
-from chainer import function_node  # NOQA
 from chainer import functions  # NOQA
-from chainer import initializer  # NOQA
 from chainer import initializers  # NOQA
 from chainer import iterators  # NOQA
-from chainer import link  # NOQA
 from chainer import links  # NOQA
-from chainer import optimizer  # NOQA
 from chainer import optimizers  # NOQA
-from chainer import reporter  # NOQA
-from chainer import serializer  # NOQA
 from chainer import serializers  # NOQA
 from chainer import training  # NOQA
-from chainer import variable  # NOQA
 
 
 # import class and function

chainer/configuration.py

@@ -1,4 +1,3 @@
-from __future__ import print_function
 import contextlib
 import sys
 import threading
@@ -79,7 +78,7 @@ def _print_attrs(obj, keys, file):
     max_len = max(len(key) for key in keys)
     for key in keys:
         spacer = ' ' * (max_len - len(key))
-        print(u'{} {}{}'.format(key, spacer, getattr(obj, key)), file=file)
+        file.write(u'{} {}{}\n'.format(key, spacer, getattr(obj, key)))
 
 
 global_config = GlobalConfig()

chainer/dataset/__init__.py

@@ -1,10 +1,4 @@
-from chainer.dataset import convert  # NOQA
-from chainer.dataset import dataset_mixin  # NOQA
-from chainer.dataset import download  # NOQA
-from chainer.dataset import iterator  # NOQA
-
-
-# import class and function
+# import classes and functions
 from chainer.dataset.convert import concat_examples  # NOQA
 from chainer.dataset.convert import ConcatWithAsyncTransfer  # NOQA
 from chainer.dataset.convert import to_device  # NOQA

chainer/dataset/download.py

@@ -1,13 +1,13 @@
-from __future__ import print_function
 import hashlib
 import os
 import shutil
 import sys
-import tempfile
 
 import filelock
 from six.moves.urllib import request
 
+from chainer import utils
+
 
 _dataset_root = os.environ.get('CHAINER_DATASET_ROOT',
                                os.path.expanduser('~/.chainer/dataset'))
@@ -101,15 +101,12 @@ def cached_download(url):
         if os.path.exists(cache_path):
             return cache_path
 
-    temp_root = tempfile.mkdtemp(dir=cache_root)
-    try:
+    with utils.tempdir(dir=cache_root) as temp_root:
         temp_path = os.path.join(temp_root, 'dl')
-        print('Downloading from {}...'.format(url), file=sys.stderr)
+        sys.stderr.write('Downloading from {}...\n'.format(url))
         request.urlretrieve(url, temp_path)
         with filelock.FileLock(lock_path):
             shutil.move(temp_path, cache_path)
-    finally:
-        shutil.rmtree(temp_root)
 
     return cache_path
 
@@ -141,10 +138,6 @@ def cache_or_load_file(path, creator, loader):
     if os.path.exists(path):
         return loader(path)
 
-    file_name = os.path.basename(path)
-    temp_dir = tempfile.mkdtemp()
-    temp_path = os.path.join(temp_dir, file_name)
-
     try:
         os.makedirs(_dataset_root)
     except OSError:
@@ -153,12 +146,12 @@ def cache_or_load_file(path, creator, loader):
 
     lock_path = os.path.join(_dataset_root, '_create_lock')
 
-    try:
+    with utils.tempdir() as temp_dir:
+        file_name = os.path.basename(path)
+        temp_path = os.path.join(temp_dir, file_name)
         content = creator(temp_path)
         with filelock.FileLock(lock_path):
             if not os.path.exists(path):
                 shutil.move(temp_path, path)
-    finally:
-        shutil.rmtree(temp_dir)
 
     return content

chainer/datasets/__init__.py

@@ -1,16 +1,4 @@
-from chainer.datasets import cifar  # NOQA
-from chainer.datasets import dict_dataset  # NOQA
-from chainer.datasets import fashion_mnist  # NOQA
-from chainer.datasets import image_dataset  # NOQA
-from chainer.datasets import mnist  # NOQA
-from chainer.datasets import ptb  # NOQA
-from chainer.datasets import sub_dataset  # NOQA
-from chainer.datasets import svhn  # NOQA
-from chainer.datasets import transform_dataset  # NOQA
-from chainer.datasets import tuple_dataset  # NOQA
-
-
-# import class and function
+# import classes and functions
 from chainer.datasets.cifar import get_cifar10  # NOQA
 from chainer.datasets.cifar import get_cifar100  # NOQA
 from chainer.datasets.concatenated_dataset import ConcatenatedDataset  # NOQA

chainer/function_hooks/__init__.py

@@ -1,10 +1,4 @@
-from chainer.function_hooks import cuda_profile  # NOQA
-from chainer.function_hooks import cupy_memory_profile  # NOQA
-from chainer.function_hooks import debug_print  # NOQA
-from chainer.function_hooks import timer  # NOQA
-
-
-# import class and function
+# import classes and functions
 from chainer.function_hooks.cuda_profile import CUDAProfileHook  # NOQA
 from chainer.function_hooks.cupy_memory_profile import CupyMemoryProfileHook  # NOQA
 from chainer.function_hooks.debug_print import PrintHook  # NOQA

chainer/function_hooks/debug_print.py

@@ -1,4 +1,3 @@
-from __future__ import print_function
 import sys
 import warnings
 
@@ -60,7 +59,7 @@ def __init__(self, sep=None, end='\n', file=sys.stdout, flush=True):
         self.flush = flush
 
     def _print(self, msg):
-        print(msg, end=self.end, file=self.file)
+        self.file.write(msg + self.end)
 
     def _process(self, function, in_data, out_grad=None):
         self._print('function\t{}'.format(function.label))

chainer/function_node.py

@@ -891,8 +891,28 @@ def _backprop(outputs, inputs, grad_required, retain_grad, grads, loss_scale):
         gys = tuple([gy if not isinstance(gy, tuple) else
                      chainer.functions.add(*gy)
                      for gy in gys])
+
+        # Call pre-backward hooks
+        hooks = chainer.get_function_hooks()
+        if func._n_local_function_hooks != 0:
+            hooks = collections.OrderedDict(hooks)
+            hooks.update(func.local_function_hooks)
+        hooks = hooks.values()  # avoid six for performance
+
+        in_data = tuple([x.data for x in func.inputs])
+        out_grad_data = tuple(
+            [None if g is None else g.data for g in gys])
+        cuda.get_device_from_array(*in_data).use()
+
+        for hook in hooks:
+            hook.backward_preprocess(func, in_data, out_grad_data)
+
         new_gxs = func.backward_accumulate(input_indexes, gys, gxs)
 
+        # Call post-backward hooks
+        for hook in hooks:
+            hook.backward_postprocess(func, in_data, out_grad_data)
+
         # Delete output gradients that are not required to return
         for y_ref in func.outputs:
             y = y_ref()

chainer/functions/__init__.py

@@ -311,4 +311,4 @@
 from chainer.functions.util.forget import Forget  # NOQA
 
 # Aliases
-mean = average
+from chainer.functions.math.average import average as mean  # NOQA

chainer/functions/connection/deconvolution_2d.py

@@ -122,8 +122,7 @@ def forward_cpu(self, inputs):
             # Grouped convolution implementation
             return self._forward_grouped_convolution(x, W, b)
 
-        elif ((self.dy == 1 and self.dx == 1)
-              and intel64.should_use_ideep('>=auto')
+        elif (intel64.should_use_ideep('>=auto')
               and intel64.inputs_all_ready(inputs)):
             # iDeep implementation
             self._use_ideep = True