nudles commented on a change in pull request #586: Add GlobalAVGPool operator
for autograd and onnx
URL: https://github.com/apache/singa/pull/586#discussion_r372954175
##########
File path: test/python/test_operation.py
##########
@@ -2745,5 +2745,97 @@ def test_prelu_broadcast_cpu(self):
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx0)),
grad0, decimal=5)
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx1)),
grad1, decimal=5)
+ def test_globalaveragepool_cpu(self):
+ X = np.array([[[
+ [1, 2, 3],
+ [4, 5, 6],
+ [7, 8, 9],
+ ]]]).astype(np.float32)
+ XT = np.array([[[[5]]]]).astype(np.float32)
+ DY = np.ones((1, 1, 1, 1), dtype=np.float32)
+
+ x = tensor.from_numpy(X)
+ x.to_device(cpu_dev)
+ dy = tensor.from_numpy(DY)
+ dy.to_device(cpu_dev)
+
+ result = autograd.globalaveragepool(x)
+ dx = result.creator.backward(dy.data)
+
+ DX = np.ones(X.shape, dtype=np.float32)
+ DX = np.multiply(DX, DY) / np.prod(X.shape[2:])
+
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT,
decimal=5)
+
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)),
DX, decimal=5)
+
+ def test_globalaveragepool_cpu_channel_last(self):
+ X = np.array([[
+ [[1], [2], [3]],
+ [[4], [5], [6]],
+ [[7], [8], [9]],
+ ]]).astype(np.float32)
+ XT = np.array([[[[5]]]]).astype(np.float32)
+ DY = np.ones((1, 1, 1, 1), dtype=np.float32)
+
+ x = tensor.from_numpy(X)
+ x.to_device(cpu_dev)
+ dy = tensor.from_numpy(DY)
+ dy.to_device(cpu_dev)
+
+ result = autograd.globalaveragepool(x, 'channel_last')
+ dx = result.creator.backward(dy.data)
+
+ DX = np.ones(X.shape, dtype=np.float32)
+ DX = np.multiply(DX, DY) / np.prod(X.shape[1:-1])
+
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), XT,
decimal=5)
+
np.testing.assert_array_almost_equal(tensor.to_numpy(tensor.from_raw_tensor(dx)),
DX, decimal=5)
+
+ def test_globalaveragepool_gpu(self):
+ X = np.array([[[
Review comment:
can you merge the code for gpu and cpu test?
many lines are repeated..
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