def run_static_mode(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
static_out = dygraph_to_static_func(self.dygraph_func)(
self.input, self.iter_num)
exe = fluid.Executor(self.place)
numpy_res = exe.run(main_program, fetch_list=static_out)
return numpy_res
def run_static_mode(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
tensor_list = dygraph_to_static_func(self.dygraph_func)(self.input)
exe = fluid.Executor(self.place)
static_res = exe.run(main_program, fetch_list=tensor_list[0])
return static_res[0]
def _run_static(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
static_func = dygraph_to_static_func(self.dyfunc)
out = static_func(self.len)
exe = fluid.Executor(self.place)
ret = exe.run(main_program, fetch_list=out)
return ret
def get_static_output(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
static_out = dygraph_to_static_func(self.dygraph_func)(self.input)
exe = fluid.Executor(self.place)
static_res = exe.run(main_program, fetch_list=static_out)
return static_res[0]
def _run_static(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
x_v = fluid.layers.assign(self.x)
# Transform into static graph
out = dygraph_to_static_func(self.dyfunc)(x_v)
exe = fluid.Executor(place)
ret = exe.run(main_program, fetch_list=out)
return ret
def _run_static(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
x_var = fluid.layers.assign(self.x)
static_func = dygraph_to_static_func(self.dyfunc)
out = static_func(x_var)
exe = fluid.Executor(self.place)
ret = exe.run(main_program, fetch_list=out)
return ret
def run_static_mode(self):
main_program = fluid.Program()
with fluid.program_guard(main_program):
tensor_array = dygraph_to_static_func(self.dygraph_func)(self.input)
static_out = fluid.layers.array_read(
tensor_array,
i=fluid.layers.fill_constant(
shape=[1], value=0, dtype='int64'))
exe = fluid.Executor(self.place)
numpy_res = exe.run(main_program, fetch_list=static_out)
return numpy_res[0]
def get_static_output(self):
startup_program = fluid.Program()
startup_program.random_seed = SEED
main_program = fluid.Program()
main_program.random_seed = SEED
with fluid.program_guard(main_program, startup_program):
static_out = dygraph_to_static_func(self.dygraph_func)()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(startup_program)
static_res = exe.run(main_program, fetch_list=static_out)
return static_res[0]
import paddle.fluid as fluid
from paddle.fluid.dygraph.jit import dygraph_to_static_func
def dyfunc_Linear(input):
fc = fluid.dygraph.Linear(
input_dim=10,
output_dim=5,
act='relu',
param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.99)),
bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(
value=0.5)),
)
res = fc(input)
return res
startup_program = fluid.Program()
main_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
input = np.random.random((4, 3, 10)).astype('float32')
data = fluid.layers.assign(input)
static_out = dygraph_to_static_func(dyfunc_Linear)(data)
exe = fluid.Executor(fluid.CPUPlace())
exe.run(startup_program)
static_res = exe.run(main_program, fetch_list=static_out)
print(static_res[0])
from __future__ import print_function
import paddle.fluid as fluid
import numpy as np
from paddle.fluid.dygraph.jit import dygraph_to_static_func
def dyfunc_with_if_else(x_v, label=None):
if fluid.layers.mean(x_v).numpy()[0] > 5:
x_v = x_v - 1
else:
x_v = x_v + 1
# plain if in python
if label is not None:
loss = fluid.layers.cross_entropy(x_v, label)
return loss
return x_v
main_program = fluid.Program()
with fluid.program_guard(main_program):
x = np.random.random([5, 6]).astype('float32')
x_v = fluid.layers.assign(x)
# Transform into static graph
out = dygraph_to_static_func(dyfunc_with_if_else)(x_v)
exe = fluid.Executor(fluid.CPUPlace())
ret = exe.run(main_program, fetch_list=out)
print(ret[0])