use tf.layers for pooling impl (#291)

tensorpack/callbacks/monitor.py

@@ -140,8 +140,10 @@ class Monitors(Callback):
         """
         Put a scalar.
         """
-        if isinstance(val, (np.float32, np.float64)):
+        if isinstance(val, np.floating):
             val = float(val)
+        if isinstance(val, np.integer):
+            val = int(val)
         self._dispatch(lambda m: m.process_scalar(name, val))
         s = create_scalar_summary(name, val)
         self._dispatch(lambda m: m.process_summary(s))

tensorpack/models/pool.py

@@ -7,7 +7,7 @@ import numpy as np
 
 from .shape_utils import StaticDynamicShape
 from .common import layer_register
-from ..utils.argtools import shape2d, shape4d
+from ..utils.argtools import shape2d
 from ._test import TestModel
 
 
@@ -15,20 +15,6 @@ __all__ = ['MaxPooling', 'FixedUnPooling', 'AvgPooling', 'GlobalAvgPooling',
            'BilinearUpSample']
 
 
-def _Pooling(func, x, shape, stride, padding, data_format):
-    padding = padding.upper()
-    shape = shape4d(shape, data_format=data_format)
-    if stride is None:
-        stride = shape
-    else:
-        stride = shape4d(stride, data_format=data_format)
-
-    return func(x, ksize=shape,
-                strides=stride, padding=padding,
-                data_format=data_format,
-                name='output')
-
-
 @layer_register(log_shape=True)
 def MaxPooling(x, shape, stride=None, padding='VALID', data_format='NHWC'):
     """
@@ -43,8 +29,9 @@ def MaxPooling(x, shape, stride=None, padding='VALID', data_format='NHWC'):
     Returns:
         tf.Tensor named ``output``.
     """
-    return _Pooling(tf.nn.max_pool, x, shape, stride, padding,
-                    data_format=data_format)
+    ret = tf.layers.max_pooling2d(x, shape, stride, padding,
+                                  'channels_last' if data_format == 'NHWC' else 'channels_first')
+    return tf.identity(ret, name='output')
 
 
 @layer_register(log_shape=True)
@@ -61,8 +48,9 @@ def AvgPooling(x, shape, stride=None, padding='VALID', data_format='NHWC'):
     Returns:
         tf.Tensor named ``output``.
     """
-    return _Pooling(tf.nn.avg_pool, x, shape, stride, padding,
-                    data_format=data_format)
+    ret = tf.layers.average_pooling2d(x, shape, stride, padding,
+                                      'channels_last' if data_format == 'NHWC' else 'channels_first')
+    return tf.identity(ret, name='output')
 
 
 @layer_register(log_shape=True)

tensorpack/tfutils/varmanip.py

@@ -73,7 +73,7 @@ class SessionUpdate(object):
                 name, val.shape, varshape))
             val = val.reshape(varshape)
 
-        # fix some common type incompatibility problem, but is certainly not enough
+        # fix some common type incompatibility problems, but not all
         def upcast(vartype, valtype):
             # allow up-casting
             if vartype == tf.float64 and valtype == np.float32: