def tensor_array_pop(array, idx):
assert isinstance(idx, int)
def cond(i, new_array):
return less_than(i, arr_len)
def body(i, new_array):
item = array_read(array=array, i=i)
array_write(item, array_length(new_array), new_array)
i = increment(i)
return i, new_array
arr_len = array_length(array)
if idx < 0:
idx = idx + arr_len
else:
idx = fill_constant(shape=[1], dtype="int64", value=idx)
pop_item = array_read(array, idx)
new_array = slice_tensor_array(array, 0, idx)
i = idx + 1
_, new_array = while_loop(cond, body, [i, new_array])
assign(input=new_array, output=array)
return pop_item
def static_func(x):
x = fluid.layers.assign(x)
iter_num = fluid.layers.fill_constant(shape=[1], value=3, dtype='int32')
a = fluid.layers.create_array(dtype='float32')
i = 0
a = fluid.dygraph.dygraph_to_static.variable_trans_func.to_static_variable(
a)
i = fluid.dygraph.dygraph_to_static.variable_trans_func.to_static_variable(
i)
iter_num = (fluid.dygraph.dygraph_to_static.variable_trans_func.
to_static_variable(iter_num))
x = fluid.dygraph.dygraph_to_static.variable_trans_func.to_static_variable(
x)
def while_condition_0(a, i, iter_num, x):
return i < iter_num
def while_body_0(a, i, iter_num, x):
fluid.layers.array_write(x=x, i=fluid.layers.array_length(a), array=a)
i += 1
return a, i, iter_num, x
a, i, iter_num, x = fluid.layers.while_loop(while_condition_0,
while_body_0,
[a, i, iter_num, x])
length = layers.array_length(a)
layers.Print(length)
return a[0]
def test_array_length(self):
tmp = layers.zeros(shape=[10], dtype='int32')
i = layers.fill_constant(shape=[1], dtype='int64', value=10)
arr = layers.array_write(tmp, i=i)
arr_len = layers.array_length(arr)
cpu = core.CPUPlace()
exe = Executor(cpu)
result = exe.run(fetch_list=[arr_len])[0]
self.assertEqual(11, result[0])
def test_array_length(self):
tmp = layers.zeros(shape=[10], dtype='int32')
i = layers.fill_constant(shape=[1], dtype='int64', value=10)
arr = layers.array_write(tmp, i=i)
arr_len = layers.array_length(arr)
cpu = core.CPUPlace()
exe = Executor(cpu)
result = exe.run(fetch_list=[arr_len])[0]
self.assertEqual(11, result[0])
def dygraph_func(x):
x = fluid.dygraph.to_variable(x)
iter_num = fluid.layers.fill_constant(shape=[1], value=3, dtype="int32")
a = []
i = 0
while i < iter_num:
a.append(x)
i += 1
length = layers.array_length(a)
layers.Print(length)
return a[0]
def set_program_and_run(self, main_program, case_num):
with fluid.program_guard(main_program):
x = [
fluid.data(name='x0', shape=self.shape, dtype="float32"),
fluid.data(name='x1', shape=self.shape, dtype="float32"),
fluid.data(name='x2', shape=self.shape, dtype="float32")
]
for each_x in x:
each_x.stop_gradient = False
arr = layers.create_array(dtype="float32")
for i in range(3):
idx = layers.array_length(arr)
arr = layers.array_write(x=x[i], i=idx, array=arr)
if case_num == 1:
self.sliced_arr = output = arr[0]
elif case_num == 2:
end = fluid.layers.array_length(
arr) - 1 # dtype of end is int64
self.sliced_arr = slice_arr = arr[self.start:end]
output, _ = fluid.layers.tensor_array_to_tensor(slice_arr,
axis=self.axis,
use_stack=True)
elif case_num == 3:
value_int64 = fluid.layers.fill_constant([1], "int64",
2147483648)
self.sliced_arr = slice_arr = arr[self.start:value_int64]
output, _ = fluid.layers.tensor_array_to_tensor(slice_arr,
axis=self.axis,
use_stack=True)
loss = fluid.layers.reduce_sum(output)
fluid.backward.append_backward(loss)
g_vars = list(
map(main_program.global_block().var,
[each_x.name + "@GRAD" for each_x in x]))
self.out, self.g_x0, self.g_x1, self.g_x2 = \
self.exe.run(main_program,
feed = {'x0': self.data,
'x1': self.data,
'x2': self.data},
fetch_list=[output] + g_vars)
def body(i, new_array):
item = array_read(array=array, i=i)
array_write(item, array_length(new_array), new_array)
i = increment(i)
return i, new_array