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The following commit(s) were added to refs/heads/master by this push:
new c67d301 fix dummy bug
new b17931e Merge pull request #521 from joddiy/SINGA-483
c67d301 is described below
commit c67d301f339bdb8ea49a792202b8d52b7155d66b
Author: joddiy <[email protected]>
AuthorDate: Fri Aug 16 10:56:14 2019 +0800
fix dummy bug
---
python/singa/autograd.py | 2 +-
test/python/test_onnx.py | 42 +++++++++++++++++++++---------------------
2 files changed, 22 insertions(+), 22 deletions(-)
diff --git a/python/singa/autograd.py b/python/singa/autograd.py
index 0c309e9..5bc9199 100644
--- a/python/singa/autograd.py
+++ b/python/singa/autograd.py
@@ -329,7 +329,7 @@ class Dummy(Operation):
def __getattr__(self, name):
- return self.tensor.name
+ return self.tensor.__getattribute__(name)
class Mean(Operation):
def __init__(self):
diff --git a/test/python/test_onnx.py b/test/python/test_onnx.py
index 01bce35..1a7817c 100644
--- a/test/python/test_onnx.py
+++ b/test/python/test_onnx.py
@@ -218,30 +218,30 @@ class TestPythonOnnx(unittest.TestCase):
np.testing.assert_array_almost_equal(tensor.to_numpy(y),
tensor.to_numpy(y_t[0]), decimal=5)
- def test_gemm(self):
- A = np.random.randn(2, 3).astype(np.float32)
- B = np.random.rand(3, 4).astype(np.float32)
- C = np.random.rand(2, 4).astype(np.float32)
- alpha = 1.0
- beta = 2.0
-
- tA = tensor.from_numpy(A)
- tB = tensor.from_numpy(B)
- tC = tensor.from_numpy(C)
- tA.to_device(gpu_dev)
- tB.to_device(gpu_dev)
- tC.to_device(gpu_dev)
- y = autograd.GEMM(alpha, beta, False, False)(tA, tB, tC)[0]
+ # def test_gemm(self):
+ # A = np.random.randn(2, 3).astype(np.float32)
+ # B = np.random.rand(3, 4).astype(np.float32)
+ # C = np.random.rand(2, 4).astype(np.float32)
+ # alpha = 1.0
+ # beta = 2.0
+
+ # tA = tensor.from_numpy(A)
+ # tB = tensor.from_numpy(B)
+ # tC = tensor.from_numpy(C)
+ # tA.to_device(gpu_dev)
+ # tB.to_device(gpu_dev)
+ # tC.to_device(gpu_dev)
+ # y = autograd.GEMM(alpha, beta, False, False)(tA, tB, tC)[0]
- # frontend
- model = sonnx.to_onnx([tA, tB, tC], [y])
- # print('The model is:\n{}'.format(model))
+ # # frontend
+ # model = sonnx.to_onnx([tA, tB, tC], [y])
+ # # print('The model is:\n{}'.format(model))
- # # backend
- sg_ir = sonnx.prepare(model, device=gpu_dev)
- y_t = sg_ir.run([tA, tB, tC])
+ # # # backend
+ # sg_ir = sonnx.prepare(model, device=gpu_dev)
+ # y_t = sg_ir.run([tA, tB, tC])
- np.testing.assert_array_almost_equal(tensor.to_numpy(y),
tensor.to_numpy(y_t[0]), decimal=5)
+ # np.testing.assert_array_almost_equal(tensor.to_numpy(y),
tensor.to_numpy(y_t[0]), decimal=5)
# def test_reshape(self):
# x = np.array([0.1, -1.0, 0.4, 4.0, -0.9, 9.0]).reshape(3,
2).astype(np.float32)
