From be2d3ae62919652715254d77b44bb1cced949f26 Mon Sep 17 00:00:00 2001
From: minqiyang <minqiyang@baidu.com>
Date: Mon, 21 Jan 2019 14:05:25 +0800
Subject: [PATCH] Add debug info

---
 python/paddle/fluid/layer_helper.py           |   2 +
 .../tests/unittests/test_imperative_resnet.py | 326 +++++++++++++++---
 2 files changed, 282 insertions(+), 46 deletions(-)

diff --git a/python/paddle/fluid/layer_helper.py b/python/paddle/fluid/layer_helper.py
index df5591fb2a7..4d3484d6834 100644
--- a/python/paddle/fluid/layer_helper.py
+++ b/python/paddle/fluid/layer_helper.py
@@ -437,8 +437,10 @@ class LayerHelper(object):
         # NOTE(dzhwinter): some activation support inplace compution.
         # NOTE(minqiyang): currently, we don't support inplace in imperative mode
         if not force_no_inplace and core.IsInplace(act_type):
+            print("inplace")
             tmp = input_var
         else:
+            print("not inplace")
             tmp = self.create_variable_for_type_inference(dtype=input_var.dtype)
         self.append_op(
             type=act_type,
diff --git a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py
index af821dfc068..f0c10169484 100644
--- a/python/paddle/fluid/tests/unittests/test_imperative_resnet.py
+++ b/python/paddle/fluid/tests/unittests/test_imperative_resnet.py
@@ -20,12 +20,13 @@ import six
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid import core
+from paddle.fluid.layer_helper import LayerHelper
 from paddle.fluid.optimizer import SGDOptimizer
 from paddle.fluid.imperative.nn import Conv2D, Pool2D, BatchNorm, FC
 from paddle.fluid.imperative.base import to_variable
 from test_imperative_base import new_program_scope
 
-batch_size = 8
+batch_size = 1
 train_parameters = {
     "input_size": [3, 224, 224],
     "input_mean": [0.485, 0.456, 0.406],
@@ -88,11 +89,11 @@ class ConvBNLayer(fluid.imperative.Layer):
             act=None,
             bias_attr=None)
 
-        self._batch_norm = BatchNorm(num_filters, act=act)
+        #  self._batch_norm = BatchNorm(num_filters, act=act)
 
     def forward(self, inputs):
         y = self._conv(inputs)
-        y = self._batch_norm(y)
+        #  y = self._batch_norm(y)
 
         return y
 
@@ -139,7 +140,10 @@ class BottleneckBlock(fluid.imperative.Layer):
         else:
             short = self.short(inputs)
 
-        return fluid.layers.elementwise_add(x=short, y=conv2, act='relu')
+        y = fluid.layers.elementwise_add(x=short, y=conv2)
+
+        layer_helper = LayerHelper('elementwise_add_activation', act='relu')
+        return layer_helper.append_activation(y, force_no_inplace=True)
 
 
 class ResNet(fluid.imperative.Layer):
@@ -200,16 +204,233 @@ class ResNet(fluid.imperative.Layer):
 
 
 class TestImperativeResnet(unittest.TestCase):
-    def test_resnet_gpu_float32(self):
+    #  def test_resnet_gpu_float32(self):
+    #  seed = 90
+
+    #  batch_size = train_parameters["batch_size"]
+    #  with fluid.imperative.guard():
+    #  fluid.default_startup_program().random_seed = seed
+    #  fluid.default_main_program().random_seed = seed
+
+    #  resnet = ResNet()
+    #  optimizer = optimizer_setting(train_parameters)
+    #  np.random.seed(seed)
+    #  import random
+    #  random.seed = seed
+    #  train_reader = paddle.batch(
+    #  paddle.dataset.flowers.train(use_xmap=False),
+    #  batch_size=batch_size)
+
+    #  dy_param_init_value = {}
+    #  for param in fluid.default_main_program().global_block(
+    #  ).all_parameters():
+    #  dy_param_init_value[param.name] = param._numpy()
+
+    #  for batch_id, data in enumerate(train_reader()):
+    #  if batch_id >= 1:
+    #  break
+
+    #  dy_x_data = np.array(
+    #  [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
+    #  y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+    #  batch_size, 1)
+
+    #  img = to_variable(dy_x_data)
+    #  label = to_variable(y_data)
+    #  label._stop_gradient = True
+
+    #  out = resnet(img)
+    #  loss = fluid.layers.cross_entropy(input=out, label=label)
+    #  avg_loss = fluid.layers.mean(x=loss)
+
+    #  dy_out = avg_loss._numpy()
+
+    #  if batch_id == 0:
+    #  for param in fluid.default_main_program().global_block(
+    #  ).all_parameters():
+    #  if param.name not in dy_param_init_value:
+    #  dy_param_init_value[param.name] = param._numpy()
+
+    #  avg_loss._backward()
+    #  dy_grad_value = {}
+    #  for param in fluid.default_main_program().global_block(
+    #  ).all_parameters():
+    #  if not param.stop_gradient:
+    #  np_array = np.array(param._ivar._grad_ivar().value()
+    #  .get_tensor())
+    #  dy_grad_value[param.name + core.grad_var_suffix(
+    #  )] = np_array
+
+    #  optimizer.minimize(avg_loss)
+
+    #  dy_param_value = {}
+    #  for param in fluid.default_main_program().global_block(
+    #  ).all_parameters():
+    #  dy_param_value[param.name] = param._numpy()
+
+    #  with new_program_scope():
+    #  fluid.default_startup_program().random_seed = seed
+    #  fluid.default_main_program().random_seed = seed
+
+    #  exe = fluid.Executor(fluid.CUDAPlace(0))
+
+    #  resnet = ResNet()
+    #  optimizer = optimizer_setting(train_parameters)
+
+    #  np.random.seed(seed)
+    #  import random
+    #  random.seed = seed
+    #  train_reader = paddle.batch(
+    #  paddle.dataset.flowers.train(use_xmap=False),
+    #  batch_size=batch_size)
+
+    #  img = fluid.layers.data(
+    #  name='pixel', shape=[3, 224, 224], dtype='float32')
+    #  label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+    #  out = resnet(img)
+    #  loss = fluid.layers.cross_entropy(input=out, label=label)
+    #  avg_loss = fluid.layers.mean(x=loss)
+    #  optimizer.minimize(avg_loss)
+
+    #  # initialize params and fetch them
+    #  static_param_init_value = {}
+    #  static_param_name_list = []
+    #  static_grad_name_list = []
+    #  for param in fluid.default_startup_program().global_block(
+    #  ).all_parameters():
+    #  static_param_name_list.append(param.name)
+    #  for param in fluid.default_main_program().global_block(
+    #  ).all_parameters():
+    #  if not param.stop_gradient:
+    #  static_grad_name_list.append(param.name +
+    #  core.grad_var_suffix())
+
+    #  out = exe.run(fluid.default_startup_program(),
+    #  fetch_list=static_param_name_list)
+
+    #  for i in range(len(static_param_name_list)):
+    #  static_param_init_value[static_param_name_list[i]] = out[i]
+
+    #  for batch_id, data in enumerate(train_reader()):
+    #  if batch_id >= 1:
+    #  break
+
+    #  static_x_data = np.array(
+    #  [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
+    #  y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+    #  [batch_size, 1])
+
+    #  fetch_list = [avg_loss.name]
+    #  fetch_list.extend(static_param_name_list)
+    #  fetch_list.extend(static_grad_name_list)
+    #  out = exe.run(fluid.default_main_program(),
+    #  feed={"pixel": static_x_data,
+    #  "label": y_data},
+    #  fetch_list=fetch_list)
+
+    #  static_param_value = {}
+    #  static_grad_value = {}
+    #  static_out = out[0]
+    #  param_start_pos = 1
+    #  grad_start_pos = len(static_param_name_list) + param_start_pos
+    #  for i in range(param_start_pos,
+    #  len(static_param_name_list) + param_start_pos):
+    #  static_param_value[static_param_name_list[
+    #  i - param_start_pos]] = out[i]
+    #  for i in range(grad_start_pos,
+    #  len(static_grad_name_list) + grad_start_pos):
+    #  static_grad_value[static_grad_name_list[
+    #  i - grad_start_pos]] = out[i]
+
+    #  self.assertTrue(np.allclose(static_out, dy_out))
+
+    #  self.assertEqual(len(dy_param_init_value), len(static_param_init_value))
+    #  for key, value in six.iteritems(static_param_init_value):
+    #  self.assertTrue(np.allclose(value, dy_param_init_value[key]))
+
+    #  self.assertEqual(len(dy_grad_value), len(static_grad_value))
+    #  # TODO(minqiyang): find a way to align the gradient
+    #  #  for key, value in six.iteritems(static_grad_value):
+    #  #  self.assertTrue(
+    #  #  np.allclose(value, dy_grad_value[key]))
+
+    #  self.assertEqual(len(dy_param_value), len(static_param_value))
+    #  #  for key, value in six.iteritems(static_param_value):
+    #  #  self.assertTrue(np.allclose(value, dy_param_value[key]))
+
+    def test_resnet_cpu_float32(self):
         seed = 90
 
         batch_size = train_parameters["batch_size"]
-        with fluid.imperative.guard():
+        #  with fluid.imperative.guard(device=None):
+        #  fluid.default_startup_program().random_seed = seed
+        #  fluid.default_main_program().random_seed = seed
+
+        #  resnet = ResNet()
+        #  optimizer = optimizer_setting(train_parameters)
+        #  np.random.seed(seed)
+        #  import random
+        #  random.seed = seed
+        #  train_reader = paddle.batch(
+        #  paddle.dataset.flowers.train(use_xmap=False),
+        #  batch_size=batch_size)
+
+        #  dy_param_init_value = {}
+        #  for param in fluid.default_main_program().global_block(
+        #  ).all_parameters():
+        #  dy_param_init_value[param.name] = param._numpy()
+
+        #  for batch_id, data in enumerate(train_reader()):
+        #  if batch_id >= 1:
+        #  break
+
+        #  dy_x_data = np.array(
+        #  [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
+        #  y_data = np.array([x[1] for x in data]).astype('int64').reshape(
+        #  batch_size, 1)
+
+        #  img = to_variable(dy_x_data)
+        #  label = to_variable(y_data)
+        #  label._stop_gradient = True
+
+        #  out = resnet(img)
+        #  loss = fluid.layers.cross_entropy(input=out, label=label)
+        #  avg_loss = fluid.layers.mean(x=loss)
+
+        #  dy_out = avg_loss._numpy()
+
+        #  if batch_id == 0:
+        #  for param in fluid.default_main_program().global_block(
+        #  ).all_parameters():
+        #  if param.name not in dy_param_init_value:
+        #  dy_param_init_value[param.name] = param._numpy()
+
+        #  avg_loss._backward()
+        #  dy_grad_value = {}
+        #  for param in fluid.default_main_program().global_block(
+        #  ).all_parameters():
+        #  if not param.stop_gradient:
+        #  np_array = np.array(param._ivar._grad_ivar().value()
+        #  .get_tensor())
+        #  dy_grad_value[param.name + core.grad_var_suffix(
+        #  )] = np_array
+
+        #  optimizer.minimize(avg_loss)
+
+        #  dy_param_value = {}
+        #  for param in fluid.default_main_program().global_block(
+        #  ).all_parameters():
+        #  dy_param_value[param.name] = param._numpy()
+
+        with new_program_scope():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 
+            exe = fluid.Executor(fluid.CPUPlace())
+
             resnet = ResNet()
             optimizer = optimizer_setting(train_parameters)
+
             np.random.seed(seed)
             import random
             random.seed = seed
@@ -217,10 +438,32 @@ class TestImperativeResnet(unittest.TestCase):
                 paddle.dataset.flowers.train(use_xmap=False),
                 batch_size=batch_size)
 
+            img = fluid.layers.data(
+                name='pixel', shape=[3, 224, 224], dtype='float32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+            out = resnet(img)
+            loss = fluid.layers.cross_entropy(input=out, label=label)
+            avg_loss = fluid.layers.mean(x=loss)
+            optimizer.minimize(avg_loss)
+
+            # initialize params and fetch them
             dy_param_init_value = {}
+            dy_param_name_list = []
+            dy_grad_name_list = []
+            for param in fluid.default_startup_program().global_block(
+            ).all_parameters():
+                dy_param_name_list.append(param.name)
             for param in fluid.default_main_program().global_block(
             ).all_parameters():
-                dy_param_init_value[param.name] = param._numpy()
+                if not param.stop_gradient:
+                    dy_grad_name_list.append(param.name + core.grad_var_suffix(
+                    ))
+
+            out = exe.run(fluid.default_startup_program(),
+                          fetch_list=dy_param_name_list)
+
+            for i in range(len(dy_param_name_list)):
+                dy_param_init_value[dy_param_name_list[i]] = out[i]
 
             for batch_id, data in enumerate(train_reader()):
                 if batch_id >= 1:
@@ -229,46 +472,35 @@ class TestImperativeResnet(unittest.TestCase):
                 dy_x_data = np.array(
                     [x[0].reshape(3, 224, 224) for x in data]).astype('float32')
                 y_data = np.array([x[1] for x in data]).astype('int64').reshape(
-                    batch_size, 1)
-
-                img = to_variable(dy_x_data)
-                label = to_variable(y_data)
-                label._stop_gradient = True
-
-                out = resnet(img)
-                loss = fluid.layers.cross_entropy(input=out, label=label)
-                avg_loss = fluid.layers.mean(x=loss)
-
-                dy_out = avg_loss._numpy()
-
-                if batch_id == 0:
-                    for param in fluid.default_main_program().global_block(
-                    ).all_parameters():
-                        if param.name not in dy_param_init_value:
-                            dy_param_init_value[param.name] = param._numpy()
-
-                avg_loss._backward()
-                dy_grad_value = {}
-                for param in fluid.default_main_program().global_block(
-                ).all_parameters():
-                    if not param.stop_gradient:
-                        np_array = np.array(param._ivar._grad_ivar().value()
-                                            .get_tensor())
-                        dy_grad_value[param.name + core.grad_var_suffix(
-                        )] = np_array
+                    [batch_size, 1])
 
-                optimizer.minimize(avg_loss)
+                fetch_list = [avg_loss.name]
+                fetch_list.extend(dy_param_name_list)
+                fetch_list.extend(dy_grad_name_list)
+                out = exe.run(fluid.default_main_program(),
+                              feed={"pixel": dy_x_data,
+                                    "label": y_data},
+                              fetch_list=fetch_list)
 
                 dy_param_value = {}
-                for param in fluid.default_main_program().global_block(
-                ).all_parameters():
-                    dy_param_value[param.name] = param._numpy()
+                dy_grad_value = {}
+                dy_out = out[0]
+                param_start_pos = 1
+                grad_start_pos = len(dy_param_name_list) + param_start_pos
+                for i in range(param_start_pos,
+                               len(dy_param_name_list) + param_start_pos):
+                    dy_param_value[dy_param_name_list[i -
+                                                      param_start_pos]] = out[i]
+                for i in range(grad_start_pos,
+                               len(dy_grad_name_list) + grad_start_pos):
+                    dy_grad_value[dy_grad_name_list[i - grad_start_pos]] = out[
+                        i]
 
         with new_program_scope():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
 
-            exe = fluid.Executor(fluid.CUDAPlace(0))
+            exe = fluid.Executor(fluid.CPUPlace())
 
             resnet = ResNet()
             optimizer = optimizer_setting(train_parameters)
@@ -345,15 +577,17 @@ class TestImperativeResnet(unittest.TestCase):
             self.assertTrue(np.allclose(value, dy_param_init_value[key]))
 
         self.assertEqual(len(dy_grad_value), len(static_grad_value))
-        # TODO(minqiyang): find a way to align the gradient
-        #  for key, value in six.iteritems(static_grad_value):
-        #  self.assertTrue(
-        #  np.allclose(value, dy_grad_value[key]))
+        for key, value in six.iteritems(static_grad_value):
+            if not np.allclose(value, dy_grad_value[key]):
+                #  print(key, value, dy_grad_value[key])
+                print(key)
+            #  self.assertTrue(
+            #  np.allclose(value, dy_grad_value[key]))
 
         self.assertEqual(len(dy_param_value), len(static_param_value))
-        #  for key, value in six.iteritems(static_param_value):
-
-    #  self.assertTrue(np.allclose(value, dy_param_value[key]))
+        for key, value in six.iteritems(static_param_value):
+            print(key)
+            #  self.assertTrue(np.allclose(value, dy_param_value[key]))
 
 
 if __name__ == '__main__':
-- 
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