bring some sense to import

examples/ResNet/svhn_resnet.py

@@ -21,7 +21,7 @@ from tensorpack.dataflow import imgaug
 
 """
 ResNet-110 for SVHN Digit Classification.
-Reach 1.9% validation error after 90 epochs, with 2 TitanX xxhr, 2it/s.
+Reach 1.8% validation error after 70 epochs, with 2 TitanX. 2it/s.
 You might need to adjust the learning rate schedule when running with 1 GPU.
 """
 

examples/mnist_convnet.py

@@ -8,14 +8,10 @@ import numpy as np
 import os, sys
 import argparse
 
-from tensorpack.train import *
+import tensorpack as tp
 from tensorpack.models import  *
 from tensorpack.utils import  *
-from tensorpack.tfutils.symbolic_functions import *
-from tensorpack.tfutils.summary import *
-from tensorpack.tfutils import *
 from tensorpack.callbacks import *
-from tensorpack.dataflow import *
 
 """
 MNIST ConvNet example.
@@ -60,7 +56,7 @@ class Model(ModelDesc):
         tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, cost)
 
         # compute the number of failed samples, for ClassificationError to use at test time
-        wrong = prediction_incorrect(logits, label)
+        wrong = tp.symbolic_functions.prediction_incorrect(logits, label)
         nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
         tf.add_to_collection(
@@ -72,7 +68,7 @@ class Model(ModelDesc):
                          name='regularize_loss')
         tf.add_to_collection(MOVING_SUMMARY_VARS_KEY, wd_cost)
 
-        add_param_summary([('.*/W', ['histogram'])])   # monitor histogram of all W
+        tp.summary.add_param_summary([('.*/W', ['histogram'])])   # monitor histogram of all W
         return tf.add_n([wd_cost, cost], name='cost')
 
 def get_config():
@@ -81,22 +77,22 @@ def get_config():
         os.path.join('train_log', basename[:basename.rfind('.')]))
 
     # prepare dataset
-    dataset_train = BatchData(dataset.Mnist('train'), 128)
-    dataset_test = BatchData(dataset.Mnist('test'), 256, remainder=True)
+    dataset_train = tp.BatchData(tp.dataset.Mnist('train'), 128)
+    dataset_test = tp.BatchData(tp.dataset.Mnist('test'), 256, remainder=True)
     step_per_epoch = dataset_train.size()
 
     # prepare session
-    sess_config = get_default_sess_config()
+    sess_config = tp.get_default_sess_config()
     sess_config.gpu_options.per_process_gpu_memory_fraction = 0.5
 
     lr = tf.train.exponential_decay(
         learning_rate=1e-3,
-        global_step=get_global_step_var(),
+        global_step=tp.get_global_step_var(),
         decay_steps=dataset_train.size() * 10,
         decay_rate=0.3, staircase=True, name='learning_rate')
     tf.scalar_summary('learning_rate', lr)
 
-    return TrainConfig(
+    return tp.TrainConfig(
         dataset=dataset_train,
         optimizer=tf.train.AdamOptimizer(lr),
         callbacks=Callbacks([
@@ -125,5 +121,5 @@ if __name__ == '__main__':
         config = get_config()
         if args.load:
             config.session_init = SaverRestore(args.load)
-        SimpleTrainer(config).train()
+        tp.SimpleTrainer(config).train()
 

tensorpack/__init__.py

+# -*- coding: utf-8 -*-
+# File: __init__.py
+# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+
+import models
+import train
+import utils
+import tfutils
+import callbacks
+import dataflow
+
+from .train import *
+from .models import *
+from .utils import *
+from .tfutils import *
+from .callbacks import *
+from .dataflow import *

tensorpack/callbacks/__init__.py

@@ -8,6 +8,7 @@ import os
 def _global_import(name):
     p = __import__(name, globals(), locals(), level=1)
     lst = p.__all__ if '__all__' in dir(p) else dir(p)
+    del globals()[name]
     for k in lst:
         globals()[k] = p.__dict__[k]
 

tensorpack/dataflow/__init__.py

@@ -3,6 +3,7 @@
 #  Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 from pkgutil import walk_packages
+import importlib
 import os
 import os.path
 
@@ -12,10 +13,11 @@ from . import imgaug
 def _global_import(name):
     p = __import__(name, globals(), locals(), level=1)
     lst = p.__all__ if '__all__' in dir(p) else dir(p)
+    del globals()[name]
     for k in lst:
         globals()[k] = p.__dict__[k]
 
-__SKIP = ['dftools', 'dataset']
+__SKIP = ['dftools', 'dataset', 'imgaug']
 for _, module_name, _ in walk_packages(
         [os.path.dirname(__file__)]):
     if not module_name.startswith('_') and \

tensorpack/dataflow/imgaug/__init__.py

@@ -10,6 +10,7 @@ __all__ = []
 def global_import(name):
     p = __import__(name, globals(), locals(), level=1)
     lst = p.__all__ if '__all__' in dir(p) else dir(p)
+    del globals()[name]
     for k in lst:
         globals()[k] = p.__dict__[k]
 

tensorpack/dataflow/imgaug/paste.py

@@ -63,8 +63,8 @@ class CenterPaste(ImageAugmentor):
 
         background = self.background_filler.fill(
             self.background_shape, img.arr)
-        h0 = (self.background_shape[0] - img_shape[0]) * 0.5
-        w0 = (self.background_shape[1] - img_shape[1]) * 0.5
+        h0 = int((self.background_shape[0] - img_shape[0]) * 0.5)
+        w0 = int((self.background_shape[1] - img_shape[1]) * 0.5)
         background[h0:h0+img_shape[0], w0:w0+img_shape[1]] = img.arr
         img.arr = background
         if img.coords:

tensorpack/tfutils/__init__.py

@@ -8,6 +8,7 @@ import os
 def _global_import(name):
     p = __import__(name, globals(), None, level=1)
     lst = p.__all__ if '__all__' in dir(p) else dir(p)
+    del globals()[name]
     for k in lst:
         globals()[k] = p.__dict__[k]
 

tensorpack/tfutils/summary.py

@@ -8,6 +8,9 @@ import tensorflow as tf
 from ..utils import *
 from . import get_global_step_var
 
+__all__ = ['create_summary', 'add_param_summary', 'add_activation_summary',
+           'summary_moving_average']
+
 def create_summary(name, v):
     """
     Return a tf.Summary object with name and simple scalar value v

tensorpack/train/__init__.py

@@ -11,6 +11,7 @@ def global_import(name):
     lst = p.__all__ if '__all__' in dir(p) else dir(p)
     for k in lst:
         globals()[k] = p.__dict__[k]
+    del globals()[name]
 
 for _, module_name, _ in walk_packages(
         [os.path.dirname(__file__)]):

tensorpack/utils/__init__.py

@@ -13,6 +13,7 @@ These utils should be irrelevant to tensorflow.
 def _global_import(name):
     p = __import__(name, globals(), None, level=1)
     lst = p.__all__ if '__all__' in dir(p) else dir(p)
+    del globals()[name]
     for k in lst:
         globals()[k] = p.__dict__[k]
 _global_import('naming')