auto-download svhn. update resnet perf numbers.

examples/ResNet/README.md

@@ -16,8 +16,8 @@ To train, just run:
 ```bash
 ./imagenet-resnet.py --data /path/to/original/ILSVRC --gpu 0,1,2,3 -d 18
 ```
-The speed is 1860 samples/s on 4 TitanX Pascal, and 1160 samples/s on 4 old TitanX, if your data is fast
-enough. See the [tutorial](http://tensorpack.readthedocs.io/en/latest/tutorial/efficient-dataflow.html) on how to speed up your data.
+The speed is 1310 image/s on 4 Tesla M40, if your data is fast enough.
+See the [tutorial](http://tensorpack.readthedocs.io/en/latest/tutorial/efficient-dataflow.html) on how to speed up your data.
 
 ![imagenet](imagenet-resnet.png)
 

tensorpack/dataflow/dataset/svhn.py

@@ -7,7 +7,7 @@ import os
 import numpy as np
 
 from ...utils import logger
-from ...utils.fs import get_dataset_path
+from ...utils.fs import get_dataset_path, download
 from ..base import RNGDataFlow
 
 __all__ = ['SVHNDigit']
@@ -38,8 +38,10 @@ class SVHNDigit(RNGDataFlow):
             data_dir = get_dataset_path('svhn_data')
         assert name in ['train', 'test', 'extra'], name
         filename = os.path.join(data_dir, name + '_32x32.mat')
-        assert os.path.isfile(filename), \
-            "File {} not found! Please download it from {}.".format(filename, SVHN_URL)
+        if not os.path.isfile(filename):
+            url = SVHN_URL + os.path.basename(filename)
+            logger.info("File {} not found! Downloading from {}.".format(filename, url))
+            download(url, os.path.dirname(filename))
         logger.info("Loading {} ...".format(filename))
         data = scipy.io.loadmat(filename)
         self.X = data['X'].transpose(3, 0, 1, 2)

tensorpack/train/multigpu.py

@@ -81,9 +81,10 @@ class SyncMultiGPUTrainer(MultiGPUTrainer,
         if len(config.tower) > 1:
             assert tf.test.is_gpu_available()
 
-        if not isinstance(self._input_method, StagingInputWrapper):
-            devices = ['/gpu:{}'.format(k) for k in config.tower]
-            self._input_method = StagingInputWrapper(self._input_method, devices)
+            # doens't seem to improve on single GPU
+            if not isinstance(self._input_method, StagingInputWrapper):
+                devices = ['/gpu:{}'.format(k) for k in config.tower]
+                self._input_method = StagingInputWrapper(self._input_method, devices)
 
         super(SyncMultiGPUTrainer, self).__init__(config)
         self.average_cost = average_cost

tensorpack/utils/fs.py

@@ -58,7 +58,7 @@ def download(url, dir, filename=None):
         raise
     assert size > 0, "Download an empty file!"
     # TODO human-readable size
-    print('Succesfully downloaded ' + filename + " " + str(size) + ' bytes.')
+    print('Succesfully downloaded ' + filename + ". " + str(size) + ' bytes.')
     return fpath