add dcgan ce (#732)

add dcgan ce for book/09 

09.gan/.run_ce.sh

+#!/bin/bash
+#This file is only used for continuous evaluation.
+export FLAGS_cudnn_deterministic=True
+export CUDA_VISIBLE_DEVICES=0
+python dc_gan.py --enable_ce true --epoch 1  --use_gpu True | python _ce.py
+ 

09.gan/_ce.py

+### This file is only used for continuous evaluation test!
+from __future__ import print_function
+from __future__ import division
+from __future__ import absolute_import
+import os
+import sys
+sys.path.append(os.environ['ceroot'])
+from kpi import CostKpi
+
+dcgan_d_train_cost_kpi = CostKpi(
+    'dcgan_d_train_cost',
+    0.02,
+    0,
+    actived=True,
+    desc='train cost of discriminator')
+dcgan_g_train_cost_kpi = CostKpi(
+    'dcgan_g_train_cost', 0.02, 0, actived=True, desc='train cost of generator')
+
+tracking_kpis = [dcgan_d_train_cost_kpi, dcgan_g_train_cost_kpi]
+
+
+def parse_log(log):
+    for line in log.split('\n'):
+        fs = line.strip().split('\t')
+        print(fs)
+        if len(fs) == 3 and fs[0] == 'kpis':
+            kpi_name = fs[1]
+            kpi_value = float(fs[2])
+            yield kpi_name, kpi_value
+
+
+def log_to_ce(log):
+    kpi_tracker = {}
+    for kpi in tracking_kpis:
+        kpi_tracker[kpi.name] = kpi
+        print(kpi.name)
+        print(kpi)
+    for (kpi_name, kpi_value) in parse_log(log):
+        print(kpi_name, kpi_value)
+        kpi_tracker[kpi_name].add_record(kpi_value)
+        kpi_tracker[kpi_name].persist()
+
+
+if __name__ == '__main__':
+    log = sys.stdin.read()
+    log_to_ce(log)

09.gan/dc_gan.py

@@ -41,6 +41,7 @@ add_arg('batch_size',        int,   128,          "Minibatch size.")
 add_arg('epoch',             int,   20,        "The number of epoched to be trained.")
 add_arg('output',            str,   "./output_dcgan", "The directory the model and the test result to be saved to.")
 add_arg('use_gpu',           bool,  True,       "Whether to use GPU to train.")
+add_arg('enable_ce',         bool,  False,                "If set True, enable continuous evaluation job.")
 # yapf: enable
 
 
@@ -51,6 +52,10 @@ def loss(x, label):
 
 def train(args):
 
+    if args.enable_ce:
+        np.random.seed(10)
+        fluid.default_startup_program().random_seed = 90
+
     d_program = fluid.Program()
     dg_program = fluid.Program()
 
@@ -87,10 +92,14 @@ def train(args):
     if args.use_gpu:
         exe = fluid.Executor(fluid.CUDAPlace(0))
     exe.run(fluid.default_startup_program())
+    if args.enable_ce:
+        train_reader = paddle.batch(
+            paddle.dataset.mnist.train(), batch_size=args.batch_size)
+    else:
 
-    train_reader = paddle.batch(
-        paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=60000),
-        batch_size=args.batch_size)
+        train_reader = paddle.batch(
+            paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=60000),
+            batch_size=args.batch_size)
 
     NUM_TRAIN_TIMES_OF_DG = 2
     const_n = np.random.uniform(
@@ -166,6 +175,9 @@ def train(args):
                                                   batch_id),
                     bbox_inches='tight')
                 plt.close(fig)
+        if args.enable_ce and pass_id == args.epoch - 1:
+            print("kpis\tdcgan_d_train_cost\t%f" % np.mean(losses[0]))
+            print("kpis\tdcgan_g_train_cost\t%f" % np.mean(losses[1]))
 
 
 if __name__ == "__main__":