juliusshufan commented on a change in pull request #10921: Test cases
improvement for MKLDNN on Gluon
URL: https://github.com/apache/incubator-mxnet/pull/10921#discussion_r188159032
##########
File path: tests/python/mkl/test_mkldnn.py
##########
@@ -95,122 +110,1147 @@ def __getitem__(self, key):
assert_almost_equal(y[0, 0, 0, 0], 0.016711406)
break
+def test_mkldnn_sum_inplace_with_cpu_layout():
+
+ x_shape = (32, 3, 224, 224)
+ x_npy = np.ones(x_shape)
+ y_shape = (32, 32, 222, 222)
+ y_npy = np.ones(y_shape)
+ x = mx.sym.Variable("x")
+ y = mx.sym.Variable("y")
+ z = mx.symbol.Convolution(data=x, num_filter=32, kernel=(3, 3))
+ z = mx.sym.add_n(z, y)
+ exe = z.simple_bind(ctx=mx.cpu(), x=x_shape, y=y_shape)
+ out = exe.forward(is_train=False, x=x_npy, y=y_npy)[0]
+ assert_almost_equal(out[0].asnumpy()[0, 0, 0], 1.0)
+
+@with_seed()
+def test_conv2d_mkldnn():
+ chn_list = [16, 32, 64, 128, 256, 512, 1024]
+ kernel_list = np.random.randint(low=1, high=224, size=9).tolist()
+ kernel_list.append(224)
+ batch_size = 32
+ class Net(gluon.HybridBlock):
+ def __init__(self,
+ chn_num,
+ kernel,
+ **kwargs):
+ super(Net, self).__init__(**kwargs)
+ with self.name_scope():
+ self.conv0 = gluon.nn.Conv2D(chn_num, (kernel, kernel))
+
+ def hybrid_forward(self, F, x):
+ out = self.conv0(x)
+ return out
+
+ x = mx.nd.random.uniform(-1.0, 1.0, shape=(batch_size, 3, 224, 224))
+ for i in range(len(chn_list)):
+ for j in range(len(kernel_list)):
+ net = Net(chn_list[i], kernel_list[j])
+ check_layer_forward(net, x)
Review comment:
@zheng-da this is a kind of data coverage test cases, specifically covering
the 16X channel and different spatial size from 1 to 299(these sizes normally
used with cnn networks on imagenet), the existing conv2d cases not covering the
16x channel and diversified spatial size. Similar purposes also applied to the
test cases test_mkldnn_batchnorm, and test_mkldnn_concat. Thanks.
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