add progress bar

example_cifar10.py

@@ -140,7 +140,7 @@ def get_config():
         get_model_func=get_model,
         batched_model_input=False,
         step_per_epoch=step_per_epoch,
-        max_epoch=100,
+        max_epoch=500,
     )
 
 if __name__ == '__main__':

requirements.txt

@@ -2,3 +2,4 @@ pip @ https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-0.6.0-cp27-
 termcolor
 numpy
 protobuf~=3.0.0a1
+tqdm

tensorpack/train.py

@@ -7,6 +7,7 @@ import tensorflow as tf
 from itertools import count
 import argparse
 
+import tqdm
 from utils import *
 from utils.concurrency import EnqueueThread,coordinator_guard
 from utils.callback import Callbacks
@@ -134,7 +135,8 @@ def start_train(config):
         callbacks.before_train()
         for epoch in xrange(1, config.max_epoch):
             with timed_operation('epoch {}'.format(epoch)):
-                for step in xrange(config.step_per_epoch):
+                for step in tqdm.trange(
+                        config.step_per_epoch, leave=True, mininterval=0.2):
                     if coord.should_stop():
                         return
                     # TODO if no one uses trigger_step, train_op can be

tensorpack/utils/validation_callback.py

@@ -4,6 +4,8 @@
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import tensorflow as tf
+from tqdm import tqdm
+
 from .stat import *
 from .callback import PeriodicCallback, Callback
 from .naming import *
@@ -42,17 +44,19 @@ class ValidationError(PeriodicCallback):
         cnt = 0
         err_stat = Accuracy()
         cost_sum = 0
-        for dp in self.ds.get_data():
-            feed = dict(zip(self.input_vars, dp))
+        with tqdm(total=self.ds.size()) as pbar:
+            for dp in self.ds.get_data():
+                feed = dict(zip(self.input_vars, dp))
 
-            batch_size = dp[0].shape[0]   # assume batched input
+                batch_size = dp[0].shape[0]   # assume batched input
 
-            cnt += batch_size
-            wrong, cost = self.sess.run(
-                [self.wrong_var, self.cost_var], feed_dict=feed)
-            err_stat.feed(wrong, batch_size)
-            # each batch might not have the same size in validation
-            cost_sum += cost * batch_size
+                cnt += batch_size
+                wrong, cost = self.sess.run(
+                    [self.wrong_var, self.cost_var], feed_dict=feed)
+                err_stat.feed(wrong, batch_size)
+                # each batch might not have the same size in validation
+                cost_sum += cost * batch_size
+                pbar.update()
 
         cost_avg = cost_sum / cnt
         self.writer.add_summary(