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Create new graph for pynative mode.

This commit is contained in:
gaoyong10 2021-08-25 15:14:59 +08:00
parent 823a2baeb0
commit 9fb5ce3b87
1 changed files with 144 additions and 87 deletions
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231
tests/st/control/test_cont_grad.py
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@ -56,16 +56,18 @@ def test_while_grad():
def construct(self, *inputs):
return grad_all(self.net)(*inputs)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(2), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(2), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
assert np.allclose(graph_output[1].asnumpy(), pynative_output[1].asnumpy(), 0.0001, 0.0001)
@ -201,15 +203,16 @@ def test_while_endless_case():
idx = idx + 1
return out
# graph mode
context.set_context(mode=context.GRAPH_MODE)
net = MyWhileNet()
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(2), dtype=ms.int32)
x = Tensor(np.arange(8).reshape(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
net = MyWhileNet()
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
net = MyWhileNet()
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -279,16 +282,18 @@ def test_while_with_param_forward_with_const_branch():
idx = idx + 1
return out
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = while_net
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = while_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -326,16 +331,18 @@ def test_while_opt_endless():
def construct(self, *inputs):
return grad_all(self.net)(*inputs)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32) * 3, dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32) * 3, dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -362,16 +369,18 @@ def test_no_while_call():
out = out + idx + self.param
return out
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = while_net
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = while_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -408,16 +417,18 @@ def test_while_with_param_grad_with_const_branch():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -458,16 +469,18 @@ def test_for_while_with_param_grad_with_const_branch():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -504,16 +517,18 @@ def test_for_while_with_param_grad_basic():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -550,16 +565,18 @@ def test_for_while_with_param_grad_normal():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -593,16 +610,18 @@ def test_while_with_param_basic_grad():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -636,16 +655,18 @@ def test_while_with_param_basic_grad_mul():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -680,16 +701,18 @@ def test_while_with_param_basic_grad_two():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
assert np.allclose(graph_output[1].asnumpy(), pynative_output[1].asnumpy(), 0.0001, 0.0001)
@ -726,16 +749,18 @@ def test_while_with_param_basic_grad_three():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
assert np.allclose(graph_output[1].asnumpy(), pynative_output[1].asnumpy(), 0.0001, 0.0001)
@ -774,16 +799,18 @@ def test_while_if_with_param_grad():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(3), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -817,16 +844,18 @@ def test_while_with_param_grad_not_enter_while():
def construct(self, a, b, c):
return grad_by_list(self.net, self.weights)(a, b, c)
idx = Tensor(np.array(3), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
idx = Tensor(np.array(3), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
while_net = MyWhileNet()
net = GradNet(while_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -855,16 +884,18 @@ def test_with_param_if_by_if_forward():
out = out + x * 2
return out
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(4), dtype=ms.int32)
x = Tensor(np.ones([2, 2, 2]).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -897,16 +928,18 @@ def test_with_param_if_by_if_grad_inputs():
def construct(self, *inputs):
return grad_all(self.net)(*inputs)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = GradNet(if_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = GradNet(if_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
assert np.allclose(graph_output[1].asnumpy(), pynative_output[1].asnumpy(), 0.0001, 0.0001)
@ -942,16 +975,18 @@ def test_with_param_if_by_if_grad_parameter():
def construct(self, *inputs):
return grad_by_list(self.net, self.weights)(*inputs)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(2), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = GradNet(if_net)
idx = Tensor(np.array(0), dtype=ms.int32)
end = Tensor(np.array(2), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = GradNet(if_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -983,16 +1018,18 @@ def test_with_param_if_by_if_grad_param_excute_null():
def construct(self, *inputs):
return grad_by_list(self.net, self.weights)(*inputs)
idx = Tensor(np.array(4), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = GradNet(if_net)
idx = Tensor(np.array(4), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = GradNet(if_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -1026,16 +1063,18 @@ def test_if_by_if_return_inside_grad():
def construct(self, *inputs):
return grad_by_list(self.net, self.weights)(*inputs)
idx = Tensor(np.array(1), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = GradNet(if_net)
idx = Tensor(np.array(1), dtype=ms.int32)
end = Tensor(np.array(0), dtype=ms.int32)
x = Tensor(np.random.randn(2, 2, 2).astype(np.float32), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = GradNet(if_net)
pynative_output = net(idx, end, x)
assert np.allclose(graph_output[0].asnumpy(), pynative_output[0].asnumpy(), 0.0001, 0.0001)
@ -1070,16 +1109,18 @@ def test_if_by_if_forward():
out = a + b + x
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(4), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(4), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -1141,16 +1182,18 @@ def test_if_by_if_forward_control_tuple_switch():
out = a + b + x
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -1210,16 +1253,18 @@ def test_if_by_if_forward_control_inside_net():
out = self.net(a, b, x)
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -1254,16 +1299,18 @@ def test_if_by_if_forward_use_namespace():
out = a + b + x
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -1302,16 +1349,18 @@ def test_if_by_if_forward_use_global_op():
out = a + b + x
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -1337,16 +1386,18 @@ def test_for_with_if_by_if_forward():
out = a + b + x
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -1374,16 +1425,18 @@ def test_for_with_if_by_if_forward_namespace():
out = a + b + x
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -1422,16 +1475,18 @@ def test_if_by_if_forward_const_branch_inner():
out = a + b + x
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)
@ -1470,16 +1525,18 @@ def test_if_by_if_forward_all_const_branch():
out = a + b + x
return out
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
# graph mode
context.set_context(mode=context.GRAPH_MODE)
if_net = MyIfByIfNet()
net = if_net
idx = Tensor(np.array(2), dtype=ms.float32)
end = Tensor(np.array(3), dtype=ms.float32)
x = Tensor(np.array(0), dtype=ms.float32)
graph_output = net(idx, end, x)
# pynative mode
context.set_context(mode=context.PYNATIVE_MODE)
if_net = MyIfByIfNet()
net = if_net
pynative_output = net(idx, end, x)
assert np.allclose(graph_output.asnumpy(), pynative_output.asnumpy(), 0.0001, 0.0001)