Add unittest except gradient checking (since bugs).

python/paddle/v2/fluid/tests/test_dynrnn_static_input.py

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