import unittest import paddle.v2.fluid.layers as layers from paddle.v2.fluid.executor import Executor import paddle.v2.fluid.core as core from paddle.v2.fluid.backward import append_backward_ops import numpy class TestWhileOp(unittest.TestCase): def test_simple_forward(self): d0 = layers.data( "d0", shape=[10], append_batch_size=False, data_type='float32') d1 = layers.data( "d1", shape=[10], append_batch_size=False, data_type='float32') d2 = layers.data( "d2", shape=[10], append_batch_size=False, data_type='float32') i = layers.zeros(shape=[1], dtype='int64') i.stop_gradient = True init = layers.zeros(shape=[10], dtype='float32') mem_array = layers.array_write(x=init, i=i) data_array = layers.array_write(x=d0, i=i) i = layers.increment(i) layers.array_write(d1, i, array=data_array) i = layers.increment(i) layers.array_write(d2, i, array=data_array) i = layers.zeros(shape=[1], dtype='int64') i.stop_gradient = True array_len = layers.fill_constant(shape=[1], dtype='int64', value=3) array_len.stop_gradient = True cond = layers.less_than(x=i, y=array_len) while_op = layers.While(cond=cond) with while_op.block(): d = layers.array_read(array=data_array, i=i) prev = layers.array_read(array=mem_array, i=i) result = layers.sums(input=[d, prev]) i = layers.increment(x=i, in_place=True) layers.array_write(result, i=i, array=mem_array) layers.less_than(x=i, y=array_len, cond=cond) sum_result = layers.array_read(array=mem_array, i=i) loss = layers.mean(x=sum_result) append_backward_ops(loss) cpu = core.CPUPlace() exe = Executor(cpu) d = [] for i in xrange(3): d.append(numpy.random.random(size=[10]).astype('float32')) d_tensor = [] for item in d: t = core.LoDTensor() t.set(item, cpu) d_tensor.append(t) outs = map(numpy.array, exe.run(feed={ 'd0': d_tensor[0], 'd1': d_tensor[1], 'd2': d_tensor[2] }, fetch_list=[sum_result])) self.assertAlmostEqual(numpy.sum(d), numpy.sum(outs[0]), delta=0.01) if __name__ == '__main__': unittest.main()