yangyaming
7 years ago
parent
52dad013ce
commit
6f79bbc93e
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import unittest
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import paddle.v2 as paddle
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import paddle.v2.fluid.core as core
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import paddle.v2.fluid as fluid
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from paddle.v2.fluid.backward import append_backward
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import paddle.v2.fluid.framework as framework
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from paddle.v2.fluid.framework import Program, switch_main_program
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import bisect
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import numpy as np
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fluid.default_startup_program().random_seed = 0
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np.random.seed(0)
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class TestDyRnnStaticInput(unittest.TestCase):
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def setUp(self):
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self._delta = 0.005
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self._max_sequence_len = 3
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self._program = Program()
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switch_main_program(self._program)
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self.output_dim = 10
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self.place = core.CPUPlace()
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self.prepare_x_tensor()
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self.prepare_static_input_tensor()
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self.exe = fluid.Executor(self.place)
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def prepare_x_tensor(self):
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self.x_tensor_dim = 10
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lod = [[0, 2, 3, 6]]
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shape = [lod[0][-1], self.x_tensor_dim]
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self.x_tensor_data = np.random.random(shape).astype('float32')
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self.x_tensor = core.LoDTensor()
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self.x_tensor.set_lod(lod)
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self.x_tensor.set(self.x_tensor_data, self.place)
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def prepare_static_input_tensor(self):
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self.static_input_tensor_dim = 4
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lod = [[0, 1, 3, 6]]
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shape = [lod[0][-1], self.static_input_tensor_dim]
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self.static_input_data = np.random.random(shape).astype('float32')
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self.static_input_tensor = core.LoDTensor()
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self.static_input_tensor.set_lod(lod)
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self.static_input_tensor.set(self.static_input_data, self.place)
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def fetch_value(self, var):
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fetch_outs = self.exe.run(feed={
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'x_tensor': self.x_tensor,
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'static_input_tensor': self.static_input_tensor
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},
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fetch_list=[var],
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return_numpy=False)
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return self._lodtensor_to_ndarray(fetch_outs[0])
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def _lodtensor_to_ndarray(self, lod_tensor):
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dims = lod_tensor.get_dims()
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ndarray = np.zeros(shape=dims).astype('float32')
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for i in xrange(np.product(dims)):
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ndarray.ravel()[i] = lod_tensor.get_float_element(i)
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return ndarray
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def build_graph(self, only_forward=False):
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x_tensor = fluid.layers.data(
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name='x_tensor',
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shape=[self.x_tensor_dim],
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dtype='float32',
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lod_level=1)
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x_tensor.stop_gradient = False
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static_input_tensor = fluid.layers.data(
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name='static_input_tensor',
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shape=[self.static_input_tensor_dim],
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dtype='float32',
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lod_level=1)
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static_input_tensor.stop_gradient = False
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if only_forward:
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static_input_out_array = self._program.global_block().create_var(
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name='static_input_out_array',
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type=core.VarDesc.VarType.LOD_TENSOR_ARRAY,
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dtype='float32')
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static_input_out_array.stop_gradient = True
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rnn = fluid.layers.DynamicRNN()
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with rnn.block():
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step_x = rnn.step_input(x_tensor)
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step_static_input = rnn.static_input(static_input_tensor)
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if only_forward:
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fluid.layers.array_write(
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x=step_static_input,
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i=rnn.step_idx,
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array=static_input_out_array)
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last = fluid.layers.sequence_pool(
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input=step_static_input, pool_type='last')
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projected = fluid.layers.fc(input=[step_x, last],
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size=self.output_dim)
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rnn.output(projected)
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if only_forward:
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static_input_step_outs = []
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step_idx = fluid.layers.fill_constant(
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shape=[1], dtype='int64', value=0)
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step_idx.stop_gradient = True
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for i in xrange(self._max_sequence_len):
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step_out = fluid.layers.array_read(static_input_out_array,
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step_idx)
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step_out.stop_gradient = True
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static_input_step_outs.append(step_out)
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fluid.layers.increment(x=step_idx, value=1.0, in_place=True)
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if only_forward:
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return static_input_step_outs
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last = fluid.layers.sequence_pool(input=rnn(), pool_type='last')
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loss = fluid.layers.mean(x=last)
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append_backward(loss)
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static_input_grad = self._program.global_block().var(
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framework.grad_var_name('static_input_tensor'))
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return static_input_grad, loss
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def get_seq_len_from_lod(self, lod):
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return [lod[0][i + 1] - lod[0][i] for i in xrange(len(lod[0]) - 1)]
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def get_expected_static_step_outs(self):
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x_lod = self.x_tensor.lod()
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x_seq_len = self.get_seq_len_from_lod(x_lod)
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x_seq_len_sorted = sorted(x_seq_len)
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x_sorted_indices = np.argsort(x_seq_len)[::-1]
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static_lod = self.static_input_tensor.lod()
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static_sliced = [
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self.static_input_data[static_lod[0][i]:static_lod[0][i + 1]]
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for i in xrange(len(static_lod[0]) - 1)
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]
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static_seq_len = self.get_seq_len_from_lod(static_lod)
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static_reordered = []
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for i in xrange(len(x_sorted_indices)):
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static_reordered.extend(static_sliced[x_sorted_indices[i]].tolist())
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static_seq_len_reordered = [
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static_seq_len[x_sorted_indices[i]]
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for i in xrange(len(x_sorted_indices))
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]
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static_step_outs = []
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for i in xrange(self._max_sequence_len):
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end = len(x_seq_len) - bisect.bisect_left(x_seq_len_sorted, i + 1)
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end = sum(static_seq_len_reordered[:end])
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static_step_outs.append(
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np.array(static_reordered[:end]).astype('float32'))
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return static_step_outs
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def test_step_out(self):
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static_step_outs = self.build_graph(only_forward=True)
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self.exe.run(framework.default_startup_program())
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expected_step_outs = self.get_expected_static_step_outs()
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for i in xrange(self._max_sequence_len):
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step_out = self.fetch_value(static_step_outs[i])
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self.assertTrue(np.allclose(step_out, expected_step_outs[i]))
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def test_network_gradient(self):
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pass #still have bug (seed doesn't work)
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'''
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static_input_grad, loss = self.build_graph()
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self.exe.run(framework.default_startup_program())
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actual_gradients = self.fetch_value(static_input_grad)
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static_input_shape = self.static_input_tensor.get_dims()
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numeric_gradients = np.zeros(shape=static_input_shape).astype('float32')
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#print(actual_gradient)
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print(actual_gradients)
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# calculate numeric gradients
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tensor_size = np.product(static_input_shape)
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for i in xrange(tensor_size):
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origin = self.static_input_tensor.get_float_element(i)
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x_pos = origin + self._delta
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self.static_input_tensor.set_float_element(i, x_pos)
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y_pos = self.fetch_value(loss)[0]
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x_neg = origin - self._delta
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self.static_input_tensor.set_float_element(i, x_neg)
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y_neg = self.fetch_value(loss)[0]
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self.static_input_tensor.set_float_element(i, origin)
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numeric_gradients.ravel()[i] = (y_pos - y_neg) / self._delta / 2
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print(numeric_gradients)
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'''
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if __name__ == '__main__':
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unittest.main()
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