model multiple inputs as a list

example_cifar10.py

@@ -92,18 +92,18 @@ def get_config():
 
     dataset_train = dataset.Cifar10('train')
     augmentors = [
-        RandomCrop((24, 24)),
-        Flip(horiz=True),
-        BrightnessAdd(63),
-        Contrast((0.2,1.8)),
-        MeanVarianceNormalize(all_channel=True)
+        imgaug.RandomCrop((24, 24)),
+        imgaug.Flip(horiz=True),
+        imgaug.BrightnessAdd(63),
+        imgaug.Contrast((0.2,1.8)),
+        imgaug.MeanVarianceNormalize(all_channel=True)
     ]
     dataset_train = AugmentImageComponent(dataset_train, augmentors)
     dataset_train = BatchData(dataset_train, 128)
 
     augmentors = [
-        CenterCrop((24, 24)),
-        MeanVarianceNormalize(all_channel=True)
+        imgaug.CenterCrop((24, 24)),
+        imgaug.MeanVarianceNormalize(all_channel=True)
     ]
     dataset_test = dataset.Cifar10('test')
     dataset_test = AugmentImageComponent(dataset_test, augmentors)

tensorpack/dataflow/dataset/cifar10.py

@@ -7,6 +7,7 @@ import cPickle
 import numpy
 from six.moves import urllib
 import tarfile
+import logging
 
 from ...utils import logger
 from ..base import DataFlow
@@ -24,10 +25,11 @@ def maybe_download_and_extract(dest_directory):
     filename = DATA_URL.split('/')[-1]
     filepath = os.path.join(dest_directory, filename)
     if os.path.isdir(os.path.join(dest_directory, 'cifar-10-batches-py')):
+        logger.info("Found cifar10 data in {}.".format(dest_directory))
         return
     else:
         def _progress(count, block_size, total_size):
-            sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename,
+            sys.stdout.write('\r>> Downloading %s %.1f%%' % (filepath,
                     float(count * block_size) / float(total_size) * 100.0))
             sys.stdout.flush()
         filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, reporthook=_progress)

tensorpack/dataflow/dataset/mnist.py

@@ -24,7 +24,7 @@ def maybe_download(filename, work_directory):
         os.mkdir(work_directory)
     filepath = os.path.join(work_directory, filename)
     if not os.path.exists(filepath):
-        logger.info("Downloading mnist data...")
+        logger.info("Downloading mnist data to {}...".format(filepath))
         filepath, _ = urllib.request.urlretrieve(SOURCE_URL + filename, filepath)
         statinfo = os.stat(filepath)
         logger.info('Successfully downloaded to ' + filename)

tensorpack/models/conv2d.py

@@ -31,7 +31,7 @@ def Conv2D(x, out_channel, kernel_shape,
     stride = shape4d(stride)
 
     if W_init is None:
-        W_init = tf.truncated_normal_initializer(stddev=4e-2)
+        W_init = tf.truncated_normal_initializer(stddev=1e-2)
     if b_init is None:
         b_init = tf.constant_initializer()
 

tensorpack/models/fc.py

@@ -17,7 +17,7 @@ def FullyConnected(x, out_dim, W_init=None, b_init=None, nl=tf.nn.relu):
     in_dim = x.get_shape().as_list()[1]
 
     if W_init is None:
-        W_init = tf.truncated_normal_initializer(stddev=1.0 / math.sqrt(float(in_dim)))
+        W_init = tf.truncated_normal_initializer(stddev=1 / math.sqrt(float(in_dim)))
     if b_init is None:
         b_init = tf.constant_initializer(0.0)
 

tensorpack/models/image_sample.py

@@ -39,21 +39,24 @@ def sample(img, coords):
     return sampled
 
 @layer_register()
-def ImageSample(template, mapping):
+def ImageSample(inputs):
     """
     Sample the template image, using the given coordinate, by bilinear interpolation.
+    inputs: list of [template, mapping]
     template: bxhxwxc
     mapping: bxh2xw2x2  (y, x) real-value coordinates
     Return: bxh2xw2xc
     """
+    template, mapping = inputs
     assert template.get_shape().ndims == 4 and mapping.get_shape().ndims == 4
+
     mapping = tf.maximum(mapping, 0.0)
-    tf.check_numerics(mapping, "mapping")
     lcoor = tf.cast(mapping, tf.int32)  # floor
     ucoor = lcoor + 1
 
     # has to cast to int32 and then cast back
-    # XXX tf.floor have gradient 1 w.r.t input, bug or feature?
+    # tf.floor have gradient 1 w.r.t input
+    # TODO bug fixed in #951
     diff = mapping - tf.cast(lcoor, tf.float32)
     neg_diff = 1.0 - diff   #bxh2xw2x2
 
@@ -128,7 +131,7 @@ if __name__ == '__main__':
             mapping[0,y,x,:] = np.array([y-diff+0.4, x-diff+0.5])
 
     mapv = tf.Variable(mapping)
-    output = ImageSample('sample', imv, mapv)
+    output = ImageSample('sample', [imv, mapv])
     sess = tf.Session()
     sess.run(tf.initialize_all_variables())
 

tensorpack/utils/modelutils.py

@@ -23,9 +23,9 @@ def describe_model():
 
 def get_shape_str(tensors):
     """ return the shape string for a tensor or a list of tensors"""
-    if isinstance(tensors, list):
+    if isinstance(tensors, (list, tuple)):
         shape_str = ",".join(
-            map(str(x.get_shape().as_list()), tensors))
+            map(lambda x: str(x.get_shape().as_list()), tensors))
     else:
         shape_str = str(tensors.get_shape().as_list())
     return shape_str