[GitHub] piiswrong closed pull request #10764: Add more gluon computation test on MKLDNN with memory operation(s)(slice, reshape etc.)

[GitBox <gi...@apache.org>]

piiswrong closed pull request #10764: Add more gluon computation test on MKLDNN with memory operation(s)(slice, reshape etc.)
URL: https://github.com/apache/incubator-mxnet/pull/10764
 
 
   

This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:

As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):

diff --git a/tests/python/mkl/test_mkldnn.py b/tests/python/mkl/test_mkldnn.py
index 4501c3b6261..dc9e9141a35 100644
--- a/tests/python/mkl/test_mkldnn.py
+++ b/tests/python/mkl/test_mkldnn.py
@@ -19,10 +19,15 @@
 MKL-DNN related test cases
 """
 
-import logging
-import os
-from sys import platform
+import mxnet as mx
 import numpy as np
+import sys,os,logging
+from mxnet import gluon
+from mxnet.gluon import nn
+curr_path = os.path.dirname(os.path.abspath(os.path.expanduser(__file__)))
+sys.path.append(os.path.join(curr_path, '../unittest/'))
+from common import setup_module, with_seed
+from nose.tools import raises
 from mxnet.test_utils import assert_almost_equal
 
 
@@ -35,7 +40,7 @@ def test_mkldnn_install():
     """
     logging.basicConfig(level=logging.INFO)
 
-    if not platform.startswith('linux'):
+    if not sys.platform.startswith('linux'):
         logging.info("Bypass mkldnn install test for non-Linux OS")
         return
 
@@ -144,5 +149,107 @@ def test_mkldnn_ndarray_slice():
         # trigger computation on ndarray slice
         assert_almost_equal(y[0].asnumpy()[0, 0, 0], 0.3376348)
 
+@with_seed()
+def test_reshape_before_conv():
+    """
+    This test will test gluon Conv2d computation on mkldnn with ndarray reshape
+    """
+    class Net(gluon.HybridBlock):
+        def __init__(self, **kwargs):
+            super(Net, self).__init__(**kwargs)
+            with self.name_scope():
+                self.conv0 = nn.Conv2D(10, (3, 3))
+                self.conv1 = nn.Conv2D(5, (3, 3))
+
+        def hybrid_forward(self, F, x):
+            x_reshape = x.reshape((0, 0, 20, 5))
+            y = self.conv0(x_reshape)
+            y_reshape = y.reshape((0, 0, 9, 6))
+            out = self.conv1(y_reshape)
+            return out
+    x = mx.nd.random.uniform(shape=(2, 4, 10, 10))
+    x.attach_grad()
+    net = Net()
+    net.collect_params().initialize()
+    with mx.autograd.record():
+        out1 = net(x)
+    out1.backward()
+    dx1 = x.grad
+    net.hybridize()
+    with mx.autograd.record():
+        out2 = net(x)
+    out2.backward()
+    mx.test_utils.assert_almost_equal(dx1.asnumpy(), x.grad.asnumpy(), rtol=1e-5, atol=1e-6)
+    mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-5, atol=1e-6)
+
+
+@with_seed()
+def test_slice_before_conv():
+    """
+    This test will test gluon Conv2d computation on mkldnn with ndarray slice
+    """
+    class Net(gluon.HybridBlock):
+        def __init__(self, **kwargs):
+            super(Net, self).__init__(**kwargs)
+            with self.name_scope():
+                self.conv0 = nn.Conv2D(4, (3, 3))
+                self.conv1 = nn.Conv2D(4, (3, 3))
+
+        def hybrid_forward(self, F, x):
+            x_slice = x.slice(begin=(0, 0, 0, 0), end=(2, 4, 10, 10))
+            y = self.conv0(x_slice)
+            y_slice = y.slice(begin=(1, 0, 2, 2), end=(2, 1, 7, 7))
+            out = self.conv1(y_slice)
+            return out
+    x = mx.nd.random.uniform(shape=(2, 10, 10, 10))
+    x.attach_grad()
+    net = Net()
+    net.collect_params().initialize()
+    with mx.autograd.record():
+        out1 = net(x)
+    out1.backward()
+    dx1 = x.grad
+    net.hybridize()
+    with mx.autograd.record():
+        out2 = net(x)
+    out2.backward()
+    mx.test_utils.assert_almost_equal(dx1.asnumpy(), x.grad.asnumpy(), rtol=1e-5, atol=1e-6)
+    mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-5, atol=1e-6)
+
+
+@with_seed()
+def test_slice_reshape_before_conv():
+    """
+    This test will test gluon Conv2d computation on mkldnn with ndarray reshape and slice
+    """
+    class Net(gluon.HybridBlock):
+        def __init__(self, **kwargs):
+            super(Net, self).__init__(**kwargs)
+            with self.name_scope():
+                self.conv0 = nn.Conv2D(4, (3, 3))
+                self.conv1 = nn.Conv2D(4, (3, 3))
+
+        def hybrid_forward(self, F, x):
+            x_slice = x.slice(begin=(0, 0, 0, 0), end=(2, 4, 8, 9))
+            y = self.conv0(x_slice)
+            y_reshape = y.reshape((0, 0, 14, 3))
+            out = self.conv1(y_reshape)
+            return out
+    x = mx.nd.random.uniform(shape=(2, 10, 10, 10))
+    x.attach_grad()
+    net = Net()
+    net.collect_params().initialize()
+    with mx.autograd.record():
+        out1 = net(x)
+    out1.backward()
+    dx1 = x.grad
+    net.hybridize()
+    with mx.autograd.record():
+        out2 = net(x)
+    out2.backward()
+    mx.test_utils.assert_almost_equal(dx1.asnumpy(), x.grad.asnumpy(), rtol=1e-5, atol=1e-6)
+    mx.test_utils.assert_almost_equal(out1.asnumpy(), out2.asnumpy(), rtol=1e-5, atol=1e-6)
+
+
 if __name__ == '__main__':
     test_mkldnn_install()


 

----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
 
For queries about this service, please contact Infrastructure at:
users@infra.apache.org


With regards,
Apache Git Services