IfElse Python API (#5624)

* Forward of raw if-else op

* add backward part of mnist if-else unittest

* refine fill_constant_batch_size_like layer

* add draft ifelse operator

* Complete IfElse Op

* add unittest of ifelse api

* merge baidu/develop

* Stash

* Merge develop branch

* Support int/int64 for fill_constant_batch_size_like

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

@@ -0,0 +1,154 @@
+import paddle.v2.fluid.layers as layers
+from paddle.v2.fluid.framework import Program
+from paddle.v2.fluid.executor import Executor
+from paddle.v2.fluid.optimizer import MomentumOptimizer
+import paddle.v2.fluid.core as core
+import paddle.v2 as paddle
+import unittest
+import numpy as np
+
+
+class TestMNISTIfElseOp(unittest.TestCase):
+    def test_raw_api(self):
+        kwargs = {'startup_program': Program(), 'main_program': Program()}
+        image = layers.data(
+            name='x', shape=[784], data_type='float32', **kwargs)
+
+        label = layers.data(name='y', shape=[1], data_type='int64', **kwargs)
+
+        limit = layers.fill_constant_batch_size_like(
+            input=label, dtype='int64', shape=[1], value=5.0, **kwargs)
+
+        cond = layers.less_than(x=label, y=limit, **kwargs)
+        true_image, false_image = layers.split_lod_tensor(
+            input=image, mask=cond, **kwargs)
+
+        true_out = layers.create_tensor(dtype='float32', **kwargs)
+        true_cond = layers.ConditionalBlock([true_image], **kwargs)
+
+        with true_cond.block():
+            hidden = layers.fc(input=true_image, size=100, act='tanh', **kwargs)
+            prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
+            layers.assign(input=prob, output=true_out, **kwargs)
+
+        false_out = layers.create_tensor(dtype='float32', **kwargs)
+        false_cond = layers.ConditionalBlock([false_image], **kwargs)
+
+        with false_cond.block():
+            hidden = layers.fc(input=false_image,
+                               size=200,
+                               act='tanh',
+                               **kwargs)
+            prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
+            layers.assign(input=prob, output=false_out, **kwargs)
+
+        prob = layers.merge_lod_tensor(
+            in_true=true_out, in_false=false_out, mask=cond, x=image, **kwargs)
+        loss = layers.cross_entropy(input=prob, label=label, **kwargs)
+        avg_loss = layers.mean(x=loss, **kwargs)
+
+        optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
+        optimizer.minimize(avg_loss, kwargs['startup_program'])
+
+        train_reader = paddle.batch(
+            paddle.reader.shuffle(
+                paddle.dataset.mnist.train(), buf_size=8192),
+            batch_size=200)
+
+        place = core.CPUPlace()
+        exe = Executor(place)
+
+        exe.run(kwargs['startup_program'])
+        PASS_NUM = 100
+        for pass_id in range(PASS_NUM):
+            for data in train_reader():
+                x_data = np.array(map(lambda x: x[0], data)).astype("float32")
+                y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+                y_data = np.expand_dims(y_data, axis=1)
+
+                tensor_x = core.LoDTensor()
+                tensor_x.set(x_data, place)
+
+                tensor_y = core.LoDTensor()
+                tensor_y.set(y_data, place)
+
+                outs = map(np.array,
+                           exe.run(kwargs['main_program'],
+                                   feed={'x': tensor_x,
+                                         'y': tensor_y},
+                                   fetch_list=[avg_loss]))
+                print outs[0]
+                if outs[0] < 1.0:
+                    return
+        self.assertFalse(True)
+
+    def test_ifelse(self):
+        kwargs = {'startup_program': Program(), 'main_program': Program()}
+        image = layers.data(
+            name='x', shape=[784], data_type='float32', **kwargs)
+
+        label = layers.data(name='y', shape=[1], data_type='int64', **kwargs)
+
+        limit = layers.fill_constant_batch_size_like(
+            input=label, dtype='int64', shape=[1], value=5.0, **kwargs)
+
+        cond = layers.less_than(x=label, y=limit, **kwargs)
+
+        ie = layers.IfElse(cond, **kwargs)
+
+        with ie.true_block():
+            true_image = ie.input(image)
+            hidden = layers.fc(input=true_image, size=100, act='tanh', **kwargs)
+            prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
+            ie.output(prob)
+
+        with ie.false_block():
+            false_image = ie.input(image)
+            hidden = layers.fc(input=false_image,
+                               size=200,
+                               act='tanh',
+                               **kwargs)
+            prob = layers.fc(input=hidden, size=10, act='softmax', **kwargs)
+            ie.output(prob)
+
+        prob = ie()
+        loss = layers.cross_entropy(input=prob[0], label=label, **kwargs)
+        avg_loss = layers.mean(x=loss, **kwargs)
+
+        optimizer = MomentumOptimizer(learning_rate=0.001, momentum=0.9)
+        optimizer.minimize(avg_loss, kwargs['startup_program'])
+        train_reader = paddle.batch(
+            paddle.reader.shuffle(
+                paddle.dataset.mnist.train(), buf_size=8192),
+            batch_size=200)
+
+        place = core.CPUPlace()
+        exe = Executor(place)
+
+        exe.run(kwargs['startup_program'])
+        PASS_NUM = 100
+        for pass_id in range(PASS_NUM):
+            for data in train_reader():
+                x_data = np.array(map(lambda x: x[0], data)).astype("float32")
+                y_data = np.array(map(lambda x: x[1], data)).astype("int64")
+                y_data = np.expand_dims(y_data, axis=1)
+
+                tensor_x = core.LoDTensor()
+                tensor_x.set(x_data, place)
+
+                tensor_y = core.LoDTensor()
+                tensor_y.set(y_data, place)
+
+                outs = map(np.array,
+                           exe.run(kwargs['main_program'],
+                                   feed={'x': tensor_x,
+                                         'y': tensor_y},
+                                   fetch_list=[avg_loss]))
+                print outs[0]
+                if outs[0] < 1.0:
+                    return
+        self.assertFalse(True)
+
+
+if __name__ == '__main__':
+    unittest.main()