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The following commit(s) were added to refs/heads/master by this push:
new a43f293 SINGA-474 sum operator
new c26e769 Merge pull request #505 from ShichengChen/sum
a43f293 is described below
commit a43f293d8b5f064e3e087cf1b504f052f0223777
Author: ShichengChen <[email protected]>
AuthorDate: Wed Aug 14 22:02:59 2019 +0800
SINGA-474 sum operator
---
python/singa/autograd.py | 20 ++++++++++++++++++++
test/python/test_operation.py | 44 +++++++++++++++++++++++++++++++++++++++++++
2 files changed, 64 insertions(+)
diff --git a/python/singa/autograd.py b/python/singa/autograd.py
index 15d1318..9d4a6cc 100644
--- a/python/singa/autograd.py
+++ b/python/singa/autograd.py
@@ -516,6 +516,26 @@ class SoftMax(Operation):
def softmax(x, axis=0):
return SoftMax(axis)(x)[0]
+class Sum(Operation):
+ def __init__(self):
+ super(Sum, self).__init__()
+
+ def forward(self, *l):
+ if training:
+ self.l = len(l)
+ assert(len(l)>0);
+ x = singa.Tensor(list(l[0].shape()),l[0].device())
+ x.SetFloatValue(0.0)
+ for i in range(len(l)):
+ x+=l[i]
+ return x
+
+ def backward(self, dy):
+ return [dy]*self.l
+
+
+def sum(*l):
+ return Sum()(*l)[0]
class CrossEntropy(Operation):
def __init__(self):
diff --git a/test/python/test_operation.py b/test/python/test_operation.py
index cfb0c4c..a8c6697 100755
--- a/test/python/test_operation.py
+++ b/test/python/test_operation.py
@@ -97,6 +97,50 @@ class TestPythonOperation(unittest.TestCase):
y_without_bias = conv_without_bias_0(gpu_input_tensor)
self.check_shape(y_without_bias.shape, (2, 1, 2, 2))
+ def test_sum_cpu(self):
+ x =
np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
+ x1 =
np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
+ y = x+x1
+ dy = np.ones((3, 2), dtype = np.float32)
+ grad0=dy
+ grad1=dy
+ x = tensor.from_numpy(x)
+ x1 = tensor.from_numpy(x1)
+ dy = tensor.from_numpy(dy)
+ x.to_device(cpu_dev)
+ x1.to_device(cpu_dev)
+ dy.to_device(cpu_dev)
+
+ result = autograd.sum(x,x1)
+ dx0,dx1 = result.creator.backward(dy.data)
+
+
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), y,
decimal=5)
+
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_sum_gpu(self):
+ x =
np.array([0.1,-1.0,0.4,4.0,-0.9,9.0]).reshape(3,2).astype(np.float32)
+ x1 =
np.array([0.1,1.0,0.4,4.0,0.9,9.0]).reshape(3,2).astype(np.float32)
+ y = x+x1
+ dy = np.ones((3, 2), dtype = np.float32)
+ grad0=dy
+ grad1=dy
+ x = tensor.from_numpy(x)
+ x1 = tensor.from_numpy(x1)
+ dy = tensor.from_numpy(dy)
+ x.to_device(gpu_dev)
+ x1.to_device(gpu_dev)
+ dy.to_device(gpu_dev)
+
+ result = autograd.sum(x,x1)
+ dx0,dx1 = result.creator.backward(dy.data)
+
+
+ np.testing.assert_array_almost_equal(tensor.to_numpy(result), y,
decimal=5)
+
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_conv2d_cpu(self):
# (in_channels, out_channels, kernel_size)
conv_1 = autograd.Conv2d(3, 1, 2)
