add generate flowers recordio

fluid/SE-ResNeXt-152/generate_flowers_recordio.py

@@ -0,0 +1,34 @@
+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+import paddle.dataset.flowers as flowers
+
+batch_size = 12
+data_shape = [3, 224, 224]
+
+with fluid.program_guard(fluid.Program(), fluid.Program()):
+    reader = paddle.batch(flowers.train(), batch_size=batch_size)
+    feeder = fluid.DataFeeder(
+        feed_list=[  # order is image and label
+            fluid.layers.data(
+                name='image', shape=data_shape, dtype='float32'),
+            fluid.layers.data(
+                name='label', shape=[1], dtype='int64'),
+        ],
+        place=fluid.CPUPlace())
+    fluid.recordio_writer.convert_reader_to_recordio_file(
+        './flowers_bs_12_3_224_224.recordio', reader, feeder)

fluid/SE-ResNeXt-152/train.py

@@ -222,6 +222,19 @@ def net_conf(image, label, class_dim):
     return out, avg_cost, accuracy, accuracy5
 
 
+def add_optimizer(args, avg_cost):
+    #optimizer = fluid.optimizer.SGD(learning_rate=0.002)
+    optimizer = fluid.optimizer.Momentum(
+        learning_rate=fluid.layers.piecewise_decay(
+            boundaries=[100], values=[0.1, 0.2]),
+        momentum=0.9,
+        regularization=fluid.regularizer.L2Decay(1e-4))
+    optimizer.minimize(avg_cost)
+
+    if args.use_mem_opt:
+        fluid.memory_optimize(fluid.default_main_program())
+
+
 def train_parallel_do(args):
 
     class_dim = 1000
@@ -253,16 +266,7 @@ def train_parallel_do(args):
         avg_cost = fluid.layers.mean(x=cost)
         accuracy = fluid.layers.accuracy(input=out, label=label)
 
-    #optimizer = fluid.optimizer.SGD(learning_rate=0.002)
-    optimizer = fluid.optimizer.Momentum(
-        learning_rate=fluid.layers.piecewise_decay(
-            boundaries=[100], values=[0.1, 0.2]),
-        momentum=0.9,
-        regularization=fluid.regularizer.L2Decay(1e-4))
-    opts = optimizer.minimize(avg_cost)
-
-    if args.use_mem_opt:
-        fluid.memory_optimize(fluid.default_main_program())
+    add_optimizer(args, avg_cost)
 
     place = fluid.CUDAPlace(0)
     # place = fluid.CPUPlace()
@@ -324,7 +328,7 @@ def train_parallel_exe(args):
 
     with fluid.program_guard(main, startup):
         reader = fluid.layers.open_recordio_file(
-            filename='./flowers.recordio',
+            filename='./flowers_bs_12_3_224_224.recordio',
             shapes=[[-1, 3, 224, 224], [-1, 1]],
             lod_levels=[0, 0],
             dtypes=['float32', 'int64'])
@@ -336,13 +340,7 @@ def train_parallel_exe(args):
         prediction, avg_cost, accuracy, accuracy5 = net_conf(image, label,
                                                              class_dim)
 
-        #optimizer = fluid.optimizer.SGD(learning_rate=0.002)
-        optimizer = fluid.optimizer.Momentum(
-            learning_rate=fluid.layers.piecewise_decay(
-                boundaries=[100], values=[0.1, 0.2]),
-            momentum=0.9,
-            regularization=fluid.regularizer.L2Decay(1e-4))
-        opts = optimizer.minimize(avg_cost)
+        add_optimizer(args, avg_cost)
 
         if args.use_mem_opt:
             fluid.memory_optimize(fluid.default_main_program())