[MXNET-788] Fix for issue #11733

CONTRIBUTORS.md

@@ -176,3 +176,4 @@ List of Contributors
 * [Kou Ding](https://github.com/chinakook)
 * [Istvan Fehervari](https://github.com/ifeherva)
 * [Aaron Markham](https://github.com/aaronmarkham)
+* [Sam Skalicky](https://github.com/samskalicky)

python/mxnet/test_utils.py

@@ -479,10 +479,8 @@ def assert_almost_equal(a, b, rtol=None, atol=None, names=('a', 'b'), equal_nan=
     """
     rtol = get_rtol(rtol)
     atol = get_atol(atol)
-
     if almost_equal(a, b, rtol, atol, equal_nan=equal_nan):
         return
-
     index, rel = find_max_violation(a, b, rtol, atol)
     np.set_printoptions(threshold=4, suppress=True)
     msg = npt.build_err_msg([a, b],
@@ -1203,10 +1201,10 @@ def check_speed(sym, location=None, ctx=None, N=20, grad_req=None, typ="whole",
     else:
         raise ValueError('typ can only be "whole" or "forward".')
 
-
 def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
                       arg_params=None, aux_params=None, tol=None,
-                      raise_on_err=True, ground_truth=None, equal_nan=False, use_uniform=False):
+                      raise_on_err=True, ground_truth=None, equal_nan=False,
+                      use_uniform=False, rand_type=np.float64):
     """Check symbol gives the same output for different running context
 
     Parameters
@@ -1223,6 +1221,11 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
         Optional, When flag set to true,
         random input data generated follows uniform distribution,
         not normal distribution
+    rand_type: np.dtype
+        casts the randomly generated data to this type
+        Optional, when input data is passed via arg_params,
+        defaults to np.float64 (numpy float default)
+
     Examples
     --------
     >>> # create the symbol
@@ -1283,9 +1286,11 @@ def check_consistency(sym, ctx_list, scale=1.0, grad_req='write',
     for n, arr in exe_list[0].arg_dict.items():
         if n not in arg_params:
             if use_uniform:
-                arg_params[n] = np.random.uniform(low=-0.92, high=0.92, size=arr.shape)
+                arg_params[n] = np.random.uniform(low=-0.92, high=0.92,
+                                                  size=arr.shape).astype(rand_type)
             else:
-                arg_params[n] = np.random.normal(size=arr.shape, scale=scale)
+                arg_params[n] = np.random.normal(size=arr.shape,
+                                                 scale=scale).astype(rand_type)
     for n, arr in exe_list[0].aux_dict.items():
         if n not in aux_params:
             aux_params[n] = 0

src/operator/nn/pooling.cc

@@ -377,8 +377,7 @@ We can see that Lp pooling stands between those two, in practice the most common
 
 For each window ``X``, the mathematical expression for Lp pooling is:
 
-..math::
-  f(X) = \sqrt{p}{\sum\limits_{x \in X} x^p}
+:math:`f(X) = \sqrt[p]{\sum_{x}^{X} x^p}`
 
 )code" ADD_FILELINE)
 .set_num_inputs(1)

tests/python/gpu/test_operator_gpu.py

@@ -614,13 +614,13 @@ def test_pooling_with_type():
                 {'ctx': mx.cpu(0), 'pool_data': (2, 2, 10, 10), 'type_dict': {'pool_data': np.float64}},
                 {'ctx': mx.cpu(0), 'pool_data': (2, 2, 10, 10), 'type_dict': {'pool_data': np.float32}}]
     sym = mx.sym.Pooling(kernel=(3,3), pool_type='max', pooling_convention='valid', name='pool')
-    check_consistency(sym, ctx_list)
+    check_consistency(sym, ctx_list, rand_type=np.float16)
 
     sym = mx.sym.Pooling(kernel=(3,3), pool_type='max', pooling_convention='full', name='pool')
-    check_consistency(sym, ctx_list)
+    check_consistency(sym, ctx_list, rand_type=np.float16)
 
     sym = mx.sym.Pooling(kernel=(300,300), pool_type='max', global_pool=True, name='pool')
-    check_consistency(sym, ctx_list)
+    check_consistency(sym, ctx_list, rand_type=np.float16)
 
 
 @with_seed()
@@ -765,31 +765,26 @@ def test_spatial_transformer_with_type():
     check_consistency(sym, ctx_list, grad_req="add")
 
 
-# Checking max pooling consistency over the data sets of different float types is problematic
-# as one max value in a float32 data set may not be the max value in a float16 data set.
-# This function will not be called.
-@with_seed(1234)
-def test_pooling_with_type():
+@with_seed()
+def test_pooling_with_type2():
     ctx_list = [{'ctx': mx.gpu(0), 'pool_data': (10, 2, 10, 10), 'type_dict': {'pool_data': np.float64}},
                 {'ctx': mx.gpu(0), 'pool_data': (10, 2, 10, 10), 'type_dict': {'pool_data': np.float32}},
                 {'ctx': mx.gpu(0), 'pool_data': (10, 2, 10, 10), 'type_dict': {'pool_data': np.float16}},
                 {'ctx': mx.cpu(0), 'pool_data': (10, 2, 10, 10), 'type_dict': {'pool_data': np.float64}},
                 {'ctx': mx.cpu(0), 'pool_data': (10, 2, 10, 10), 'type_dict': {'pool_data': np.float32}}]
 
     sym = mx.sym.Pooling(name='pool', kernel=(3,3), stride=(2,2), pool_type='max')
-    check_consistency(sym, ctx_list)
+    check_consistency(sym, ctx_list, rand_type=np.float16)
 
     sym = mx.sym.Pooling(name='pool', kernel=(3,3), pad=(1,1), pool_type='avg')
     check_consistency(sym, ctx_list)
 
-    # this is unstable
-    # sym = mx.sym.Pooling(name='pool', kernel=(5,5), pad=(2,2), pool_type='max')
-    # check_consistency(sym, ctx_list)
+    sym = mx.sym.Pooling(name='pool', kernel=(5,5), pad=(2,2), pool_type='max')
+    check_consistency(sym, ctx_list, rand_type=np.float16)
 
     sym = mx.sym.Pooling(name='pool', kernel=(3,3), pad=(1,1), pool_type='sum')
     check_consistency(sym, ctx_list)
 
-
 @unittest.skip("Flaky test https://github.com/apache/incubator-mxnet/issues/11517")
 @with_seed()
 def test_pooling_versions():