def test_print_add():
class Print_Add(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
self.add = P.Add()
def construct(self, x, y):
x = self.add(x, y)
self.print("input_x:", x, "input_y:", y)
return x
cap = Capture()
with capture(cap):
input_x = Tensor(3, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(7, dtype=ms.int32)
net = Print_Add()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'input_x:\nTensor(shape=[], dtype=Int32, value=7)\n'
'input_y:\nTensor(shape=[], dtype=Int32, value=4)'
}
check_output(cap.output, patterns)
def test_print_for():
class Print_For(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
def construct(self, x, y):
y = x + y
self.print("input_x before:", x, "input_y before:", y)
for _ in range(3):
y = y + 1
self.print("input_x after:", x, "input_y after:", y)
return y
cap = Capture()
with capture(cap):
input_x = Tensor(2, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(9, dtype=ms.int32)
net = Print_For()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'input_x before:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y before:\nTensor(shape=[], dtype=Int32, value=6)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=7)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=8)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=9)'
}
check_output(cap.output, patterns)
def test_print_if():
class Print_If(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
def construct(self, x, y):
self.print("input_x before:", x, "input_y before:", y)
if x < y:
self.print("input_x after:", x, "input_y after:", y)
x = x + 1
return x
cap = Capture()
with capture(cap):
input_x = Tensor(3, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(4, dtype=ms.int32)
net = Print_If()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'input_x before:\nTensor(shape=[], dtype=Int32, value=3)\n'
'input_y before:\nTensor(shape=[], dtype=Int32, value=4)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=3)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)'
}
check_output(cap.output, patterns)
def test_print_assign_add():
class Print_Assign_Add(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
self.add = P.Add()
self.para = Parameter(Tensor(1, dtype=ms.int32), name='para')
def construct(self, x, y):
self.print("before:", self.para)
self.para = x
self.print("after:", self.para)
x = self.add(self.para, y)
return x
cap = Capture()
with capture(cap):
input_x = Tensor(3, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(7, dtype=ms.int32)
net = Print_Assign_Add()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'before:\nTensor(shape=[], dtype=Int32, value=1)',
'after:\nTensor(shape=[], dtype=Int32, value=3)'
}
check_output(cap.output, patterns)
def test_print_assign_while():
class Print_Assign_While(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
self.para = Parameter(Tensor(0, dtype=ms.int32), name='para')
def construct(self, x, y):
self.print("input_x before:", x, "input_y before:", y,
"para before:", self.para)
while x < y:
self.para = x
x = self.para + 1
self.print("input_x after:", x, "input_y after:", y,
"para after:", self.para)
return x
cap = Capture()
with capture(cap):
input_x = Tensor(1, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
expect = Tensor(4, dtype=ms.int32)
net = Print_Assign_While()
out = net(input_x, input_y)
time.sleep(0.1)
np.testing.assert_array_equal(out.asnumpy(), expect.asnumpy())
patterns = {
'input_x before:\nTensor(shape=[], dtype=Int32, value=1)\n'
'input_y before:\nTensor(shape=[], dtype=Int32, value=4)\n'
'para before:\nTensor(shape=[], dtype=Int32, value=0)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=2)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
'para after:\nTensor(shape=[], dtype=Int32, value=1)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=3)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
'para after:\nTensor(shape=[], dtype=Int32, value=2)',
'input_x after:\nTensor(shape=[], dtype=Int32, value=4)\n'
'input_y after:\nTensor(shape=[], dtype=Int32, value=4)\n'
'para after:\nTensor(shape=[], dtype=Int32, value=3)'
}
check_output(cap.output, patterns)
def test_print():
class Print(Cell):
def __init__(self):
super().__init__()
self.print = P.Print()
def construct(self, x, y):
self.print("input_x:", x, "input_y:", y)
return x
cap = Capture()
with capture(cap):
input_x = Tensor(3, dtype=ms.int32)
input_y = Tensor(4, dtype=ms.int32)
net = Print()
net(input_x, input_y)
time.sleep(0.1)
patterns = {
'input_x:\nTensor(shape=[], dtype=Int32, value=3)\n'
'input_y:\nTensor(shape=[], dtype=Int32, value=4)'
}
check_output(cap.output, patterns)