forked from mindspore-Ecosystem/mindspore
int16 uint8 bool supported akg ops
This commit is contained in:
parent
2dc4dae41c
commit
e0528615e3
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@ -23,6 +23,9 @@ equal_op_info = AkgGpuRegOp("Equal") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
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.get_op_info()
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@ -23,6 +23,8 @@ greater_equal_op_info = AkgGpuRegOp("GreaterEqual") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
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.get_op_info()
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@ -23,6 +23,8 @@ lessequal_op_info = AkgGpuRegOp("LessEqual") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
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.get_op_info()
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@ -23,6 +23,9 @@ notequal_op_info = AkgGpuRegOp("NotEqual") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.I16_Default, DataType.I16_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.U8_Default, DataType.U8_Default, DataType.BOOL_Default) \
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.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default, DataType.BOOL_Default) \
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.get_op_info()
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@ -22,6 +22,10 @@ squeeze_op_info = AkgGpuRegOp("Squeeze") \
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.attr("axis", "optional", "listInt") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default) \
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.dtype_format(DataType.I16_Default, DataType.I16_Default) \
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.dtype_format(DataType.U8_Default, DataType.U8_Default) \
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.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
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.get_op_info()
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@ -22,6 +22,10 @@ squeeze_grad_op_info = AkgGpuRegOp("SqueezeGrad") \
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.attr("x_shape", "required", "listInt") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default) \
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.dtype_format(DataType.I16_Default, DataType.I16_Default) \
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.dtype_format(DataType.U8_Default, DataType.U8_Default) \
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.dtype_format(DataType.BOOL_Default, DataType.BOOL_Default) \
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.get_op_info()
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@ -22,6 +22,8 @@ tile_op_info = AkgGpuRegOp("Tile") \
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.attr("multiples", "required", "listInt") \
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.dtype_format(DataType.F16_Default, DataType.F16_Default) \
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.dtype_format(DataType.F32_Default, DataType.F32_Default) \
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.dtype_format(DataType.I32_Default, DataType.I32_Default) \
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.dtype_format(DataType.I16_Default, DataType.I16_Default) \
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.get_op_info()
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@ -65,6 +65,21 @@ def test_equal():
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y2_np = np.array([0, 1, -3]).astype(np.int32)
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y2 = Tensor(y2_np)
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expect2 = np.equal(x2_np, y2_np)
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x3_np = np.array([0, 1, 3]).astype(np.int16)
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x3 = Tensor(x3_np)
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y3_np = np.array([0, 1, -3]).astype(np.int16)
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y3 = Tensor(y3_np)
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expect3 = np.equal(x3_np, y3_np)
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x4_np = np.array([0, 1, 4]).astype(np.uint8)
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x4 = Tensor(x4_np)
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y4_np = np.array([0, 1, 3]).astype(np.uint8)
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y4 = Tensor(y4_np)
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expect4 = np.equal(x4_np, y4_np)
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x5_np = np.array([True, False, True]).astype(bool)
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x5 = Tensor(x5_np)
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y5_np = np.array([True, False, False]).astype(bool)
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y5 = Tensor(y5_np)
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expect5 = np.equal(x5_np, y5_np)
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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equal = NetEqual()
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@ -77,6 +92,17 @@ def test_equal():
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output2 = equal(x2, y2)
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assert np.all(output2.asnumpy() == expect2)
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assert output2.shape == expect2.shape
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output3 = equal(x3, y3)
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assert np.all(output3.asnumpy() == expect3)
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assert output3.shape == expect3.shape
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output4 = equal(x4, y4)
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assert np.all(output4.asnumpy() == expect4)
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assert output4.shape == expect4.shape
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output5 = equal(x5, y5)
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assert np.all(output5.asnumpy() == expect5)
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assert output5.shape == expect5.shape
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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equal = NetEqual()
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@ -89,6 +115,15 @@ def test_equal():
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output2 = equal(x2, y2)
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assert np.all(output2.asnumpy() == expect2)
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assert output2.shape == expect2.shape
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output3 = equal(x3, y3)
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assert np.all(output3.asnumpy() == expect3)
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assert output3.shape == expect3.shape
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output4 = equal(x4, y4)
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assert np.all(output4.asnumpy() == expect4)
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assert output4.shape == expect4.shape
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output5 = equal(x5, y5)
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assert np.all(output5.asnumpy() == expect5)
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assert output5.shape == expect5.shape
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@pytest.mark.level0
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@ -98,18 +133,45 @@ def test_notequal():
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x0 = Tensor(np.array([[1.2, 1], [1, 0]]).astype(np.float32))
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y0 = Tensor(np.array([[1, 2]]).astype(np.float32))
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expect0 = np.array([[True, True], [False, True]])
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x1 = Tensor(np.array([[2, 1], [1, 1]]).astype(np.int16))
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y1 = Tensor(np.array([[1, 2]]).astype(np.int16))
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expect1 = np.array([[True, True], [False, True]])
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x2 = Tensor(np.array([[2, 1], [1, 2]]).astype(np.uint8))
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y2 = Tensor(np.array([[1, 2]]).astype(np.uint8))
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expect2 = np.array([[True, True], [False, False]])
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x3 = Tensor(np.array([[False, True], [True, False]]).astype(bool))
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y3 = Tensor(np.array([[True, False]]).astype(bool))
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expect3 = np.array([[True, True], [False, False]])
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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notequal = NetNotEqual()
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output0 = notequal(x0, y0)
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assert np.all(output0.asnumpy() == expect0)
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assert output0.shape == expect0.shape
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output1 = notequal(x1, y1)
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assert np.all(output1.asnumpy() == expect1)
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assert output1.shape == expect1.shape
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output2 = notequal(x2, y2)
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assert np.all(output2.asnumpy() == expect2)
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assert output2.shape == expect2.shape
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output3 = notequal(x3, y3)
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assert np.all(output3.asnumpy() == expect3)
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assert output3.shape == expect3.shape
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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notequal = NetNotEqual()
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output0 = notequal(x0, y0)
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assert np.all(output0.asnumpy() == expect0)
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assert output0.shape == expect0.shape
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output1 = notequal(x1, y1)
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assert np.all(output1.asnumpy() == expect1)
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assert output1.shape == expect1.shape
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output2 = notequal(x2, y2)
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assert np.all(output2.asnumpy() == expect2)
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assert output2.shape == expect2.shape
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output3 = notequal(x3, y3)
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assert np.all(output3.asnumpy() == expect3)
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assert output3.shape == expect3.shape
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@pytest.mark.level0
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@ -119,15 +181,33 @@ def test_greaterqual():
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x0 = Tensor(np.array([[1.2, 1], [1, 0]]).astype(np.float32))
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y0 = Tensor(np.array([[1, 2]]).astype(np.float32))
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expect0 = np.array([[True, False], [True, False]])
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x1 = Tensor(np.array([[2, 1], [1, 1]]).astype(np.int16))
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y1 = Tensor(np.array([[1, 2]]).astype(np.int16))
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expect1 = np.array([[True, False], [True, False]])
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x2 = Tensor(np.array([[2, 1], [1, 2]]).astype(np.uint8))
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y2 = Tensor(np.array([[1, 2]]).astype(np.uint8))
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expect2 = np.array([[True, False], [True, True]])
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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gequal = NetGreaterEqual()
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output0 = gequal(x0, y0)
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assert np.all(output0.asnumpy() == expect0)
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assert output0.shape == expect0.shape
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output1 = gequal(x1, y1)
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assert np.all(output1.asnumpy() == expect1)
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assert output1.shape == expect1.shape
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output2 = gequal(x2, y2)
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assert np.all(output2.asnumpy() == expect2)
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assert output2.shape == expect2.shape
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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gequal = NetGreaterEqual()
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output0 = gequal(x0, y0)
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assert np.all(output0.asnumpy() == expect0)
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assert output0.shape == expect0.shape
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output1 = gequal(x1, y1)
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assert np.all(output1.asnumpy() == expect1)
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assert output1.shape == expect1.shape
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output2 = gequal(x2, y2)
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assert np.all(output2.asnumpy() == expect2)
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assert output2.shape == expect2.shape
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@ -38,12 +38,27 @@ def test_lessequal():
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x = Tensor(np.array([[1, 2, 3]]).astype(np.float32))
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y = Tensor(np.array([[2]]).astype(np.float32))
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expect = [[True, True, False]]
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x1 = Tensor(np.array([[1, 2, 3]]).astype(np.int16))
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y1 = Tensor(np.array([[2]]).astype(np.int16))
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expect = [[True, True, False]]
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x2 = Tensor(np.array([[1, 2, 3]]).astype(np.uint8))
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y2 = Tensor(np.array([[2]]).astype(np.uint8))
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expect = [[True, True, False]]
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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lessequal = Net()
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output = lessequal(x, y)
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assert np.all(output.asnumpy() == expect)
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output = lessequal(x1, y1)
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assert np.all(output.asnumpy() == expect)
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output = lessequal(x2, y2)
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assert np.all(output.asnumpy() == expect)
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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lessequal = Net()
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output = lessequal(x, y)
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assert np.all(output.asnumpy() == expect)
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output = lessequal(x1, y1)
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assert np.all(output.asnumpy() == expect)
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output = lessequal(x2, y2)
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assert np.all(output.asnumpy() == expect)
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@ -0,0 +1,79 @@
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# Copyright 2019 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.squeeze = P.Squeeze()
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def construct(self, tensor):
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return self.squeeze(tensor)
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def test_net_bool():
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x = np.random.randn(1, 16, 1, 1).astype(np.bool)
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net = Net()
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output = net(Tensor(x))
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print(output.asnumpy())
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assert np.all(output.asnumpy() == x.squeeze())
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def test_net_uint8():
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x = np.random.randn(1, 16, 1, 1).astype(np.uint8)
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net = Net()
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output = net(Tensor(x))
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print(output.asnumpy())
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assert np.all(output.asnumpy() == x.squeeze())
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def test_net_int16():
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x = np.random.randn(1, 16, 1, 1).astype(np.int16)
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net = Net()
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output = net(Tensor(x))
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print(output.asnumpy())
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assert np.all(output.asnumpy() == x.squeeze())
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def test_net_int32():
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x = np.random.randn(1, 16, 1, 1).astype(np.int32)
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net = Net()
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output = net(Tensor(x))
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print(output.asnumpy())
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assert np.all(output.asnumpy() == x.squeeze())
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def test_net_float16():
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x = np.random.randn(1, 16, 1, 1).astype(np.float16)
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net = Net()
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output = net(Tensor(x))
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print(output.asnumpy())
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assert np.all(output.asnumpy() == x.squeeze())
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def test_net_float32():
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x = np.random.randn(1, 16, 1, 1).astype(np.float32)
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net = Net()
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output = net(Tensor(x))
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print(output.asnumpy())
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assert np.all(output.asnumpy() == x.squeeze())
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@ -33,6 +33,27 @@ mul1 = (2, 2, 2)
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input_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.float32)
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mul2 = (1, 1, 1)
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input_32_x0 = np.arange(2).reshape((2, 1, 1)).astype(np.int32)
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mul_32_0 = (8, 1, 1)
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input_32_x1 = np.arange(32).reshape((2, 4, 4)).astype(np.int32)
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mul_32_1 = (2, 2, 2)
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input_32_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.int32)
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mul_32_2 = (1, 1, 1)
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input_16_x0 = np.arange(2).reshape((2, 1, 1)).astype(np.int16)
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mul_16_0 = (8, 1, 1)
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input_16_x1 = np.arange(32).reshape((2, 4, 4)).astype(np.int16)
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mul_16_1 = (2, 2, 2)
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input_16_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.int16)
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mul_16_2 = (1, 1, 1)
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input_8_x0 = np.arange(2).reshape((2, 1, 1)).astype(np.uint8)
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mul_8_0 = (8, 1, 1)
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input_8_x1 = np.arange(32).reshape((2, 4, 4)).astype(np.int8)
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mul_8_1 = (2, 2, 2)
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input_8_x2 = np.arange(1).reshape((1, 1, 1)).astype(np.uint8)
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mul_8_2 = (1, 1, 1)
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class Net(Cell):
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def __init__(self):
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@ -54,6 +75,46 @@ class Net(Cell):
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return output
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class Net32(Cell):
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def __init__(self):
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super(Net32, self).__init__()
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self.Tile = Tile()
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self.input_32_x0 = Parameter(initializer(Tensor(input_32_x0), input_32_x0.shape), name='x0')
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self.mul_32_0 = mul_32_0
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self.input_32_x1 = Parameter(initializer(Tensor(input_32_x1), input_32_x1.shape), name='x1')
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self.mul_32_1 = mul_32_1
|
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self.input_32_x2 = Parameter(initializer(Tensor(input_32_x2), input_32_x2.shape), name='x2')
|
||||
self.mul_32_2 = mul_32_2
|
||||
|
||||
@ms_function
|
||||
def construct(self):
|
||||
output = (self.Tile(self.input_32_x0, self.mul_32_0),
|
||||
self.Tile(self.input_32_x1, self.mul_32_1),
|
||||
self.Tile(self.input_32_x2, self.mul_32_2))
|
||||
return output
|
||||
|
||||
|
||||
class Net16(Cell):
|
||||
def __init__(self):
|
||||
super(Net16, self).__init__()
|
||||
self.Tile = Tile()
|
||||
|
||||
self.input_16_x0 = Parameter(initializer(Tensor(input_16_x0), input_16_x0.shape), name='x0')
|
||||
self.mul_16_0 = mul_16_0
|
||||
self.input_16_x1 = Parameter(initializer(Tensor(input_16_x1), input_16_x1.shape), name='x1')
|
||||
self.mul_16_1 = mul_16_1
|
||||
self.input_16_x2 = Parameter(initializer(Tensor(input_16_x2), input_16_x2.shape), name='x2')
|
||||
self.mul_16_2 = mul_16_2
|
||||
|
||||
@ms_function
|
||||
def construct(self):
|
||||
output = (self.Tile(self.input_16_x0, self.mul_16_0),
|
||||
self.Tile(self.input_16_x1, self.mul_16_1),
|
||||
self.Tile(self.input_16_x2, self.mul_16_2))
|
||||
return output
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
|
|
@ -78,3 +139,55 @@ def test_tile():
|
|||
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
||||
assert np.all(diff2 < error2)
|
||||
assert output[2].shape == expect2.shape
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_tile_32():
|
||||
net = Net32()
|
||||
output = net()
|
||||
|
||||
expect0 = np.tile(input_32_x0, mul_32_0)
|
||||
diff0 = output[0].asnumpy() - expect0
|
||||
error0 = np.ones(shape=expect0.shape) * 1.0e-5
|
||||
assert np.all(diff0 < error0)
|
||||
assert output[0].shape == expect0.shape
|
||||
|
||||
expect1 = np.tile(input_32_x1, mul_32_1)
|
||||
diff1 = output[1].asnumpy() - expect1
|
||||
error1 = np.ones(shape=expect1.shape) * 1.0e-5
|
||||
assert np.all(diff1 < error1)
|
||||
assert output[1].shape == expect1.shape
|
||||
|
||||
expect2 = np.tile(input_32_x2, mul_32_2)
|
||||
diff2 = output[2].asnumpy() - expect2
|
||||
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
||||
assert np.all(diff2 < error2)
|
||||
assert output[2].shape == expect2.shape
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_tile_16():
|
||||
net = Net16()
|
||||
output = net()
|
||||
|
||||
expect0 = np.tile(input_16_x0, mul_16_0)
|
||||
diff0 = output[0].asnumpy() - expect0
|
||||
error0 = np.ones(shape=expect0.shape) * 1.0e-5
|
||||
assert np.all(diff0 < error0)
|
||||
assert output[0].shape == expect0.shape
|
||||
|
||||
expect1 = np.tile(input_16_x1, mul_16_1)
|
||||
diff1 = output[1].asnumpy() - expect1
|
||||
error1 = np.ones(shape=expect1.shape) * 1.0e-5
|
||||
assert np.all(diff1 < error1)
|
||||
assert output[1].shape == expect1.shape
|
||||
|
||||
expect2 = np.tile(input_16_x2, mul_16_2)
|
||||
diff2 = output[2].asnumpy() - expect2
|
||||
error2 = np.ones(shape=expect2.shape) * 1.0e-5
|
||||
assert np.all(diff2 < error2)
|
||||
assert output[2].shape == expect2.shape
|
||||
|
|
|
|||
Loading…
Reference in New Issue