clean imports

tensorpack/RL/gymenv.py

@@ -11,8 +11,10 @@ try:
     gym.undo_logger_setup()
     # https://github.com/openai/gym/pull/199
     # not sure does it cause other problems
+    __all__ = ['GymEnv']
 except ImportError:
     logger.warn("Cannot import gym. GymEnv won't be available.")
+    __all__ = []
 
 import threading
 
@@ -20,7 +22,6 @@ from ..utils.fs import *
 from ..utils.stat import *
 from .envbase import RLEnvironment, DiscreteActionSpace
 
-__all__ = ['GymEnv']
 
 _ALE_LOCK = threading.Lock()
 

tensorpack/callbacks/base.py

@@ -8,8 +8,6 @@ import os
 import time
 from abc import abstractmethod, ABCMeta
 
-from ..utils import *
-
 __all__ = ['Callback', 'PeriodicCallback']
 
 class Callback(object):

tensorpack/callbacks/group.py

@@ -7,8 +7,8 @@ from contextlib import contextmanager
 import time
 
 from .base import Callback
-from .stat import *
-from ..utils import *
+from .stat import StatPrinter
+from ..utils import logger
 
 __all__ = ['Callbacks']
 

tensorpack/callbacks/inference.py

@@ -11,10 +11,9 @@ import six
 from six.moves import zip, map
 
 from ..dataflow import DataFlow
-from ..utils import *
-from ..utils.stat import *
-from ..tfutils import *
-from ..tfutils.summary import *
+from ..utils import get_tqdm_kwargs, logger
+from ..utils.stat import RatioCounter, BinaryStatistics
+from ..tfutils import get_op_tensor_name
 from .base import Callback
 
 __all__ = ['InferenceRunner', 'ClassificationError',

tensorpack/callbacks/stat.py

@@ -8,7 +8,7 @@ import operator
 import json
 
 from .base import Callback
-from ..utils import *
+from ..utils import logger
 
 __all__ = ['StatHolder', 'StatPrinter', 'SendStat']
 

tensorpack/models/_common.py

@@ -7,9 +7,9 @@ from functools import wraps
 import six
 import copy, os
 
-from ..tfutils import *
-from ..tfutils.modelutils import *
-from ..tfutils.summary import *
+from ..tfutils.argscope import get_arg_scope
+from ..tfutils.modelutils import get_shape_str
+from ..tfutils.summary import add_activation_summary
 from ..utils import logger
 
 # make sure each layer is only logged once

tensorpack/models/batch_norm.py

@@ -7,7 +7,7 @@ import tensorflow as tf
 from copy import copy
 import re
 
-from .model_desc import get_current_tower_context
+from ..tfutils.tower import get_current_tower_context
 from ..utils import logger, EXTRA_SAVE_VARS_KEY
 from ._common import layer_register
 

tensorpack/models/conv2d.py

@@ -6,7 +6,7 @@
 import numpy as np
 import tensorflow as tf
 import math
-from ._common import *
+from ._common import layer_register, shape2d, shape4d
 from ..utils import map_arg, logger
 
 __all__ = ['Conv2D']

tensorpack/models/fc.py

@@ -7,7 +7,7 @@ import tensorflow as tf
 import math
 
 from ._common import layer_register
-from ..tfutils.symbolic_functions import *
+from ..tfutils import symbolic_functions as symbf
 
 __all__ = ['FullyConnected']
 
@@ -26,7 +26,7 @@ def FullyConnected(x, out_dim,
     :param use_bias: whether to use bias. a boolean default to True
     :returns: a 2D tensor
     """
-    x = batch_flatten(x)
+    x = symbf.batch_flatten(x)
     in_dim = x.get_shape().as_list()[1]
 
     if W_init is None:

tensorpack/models/model_desc.py

@@ -10,85 +10,13 @@ from collections import namedtuple
 import inspect
 
 from ..utils import logger, INPUT_VARS_KEY
-from ..tfutils import *
+from ..tfutils.common import get_vars_by_names
+from ..tfutils.gradproc import CheckGradient
 
-__all__ = ['ModelDesc', 'InputVar', 'ModelFromMetaGraph',
-        'get_current_tower_context', 'TowerContext']
+__all__ = ['ModelDesc', 'InputVar', 'ModelFromMetaGraph' ]
 
 InputVar = namedtuple('InputVar', ['type', 'shape', 'name'])
 
-_CurrentTowerContext = None
-
-class TowerContext(object):
-    def __init__(self, tower_name, is_training=None):
-        """ tower_name: 'tower0', 'towerp0', or '' """
-        self._name = tower_name
-        if is_training is None:
-            is_training = not self._name.startswith('towerp')
-        self._is_training = is_training
-
-    @property
-    def is_main_training_tower(self):
-        return self.is_training and (self._name == '' or self._name == 'tower0')
-
-    @property
-    def is_main_tower(self):
-        return self._name == '' or self._name == 'tower0'
-
-    @property
-    def is_training(self):
-        return self._is_training
-
-    @property
-    def name(self):
-        return self._name
-
-    def get_variable_on_tower(self, *args, **kwargs):
-        """
-        Get a variable for this tower specifically, without reusing.
-        Tensorflow doesn't allow reuse=False scope under a
-        reuse=True scope. This method provides a work around.
-        See https://www.tensorflow.org/versions/master/how_tos/variable_scope/index.html#basics-of-tfvariable-scope
-
-        :param args, kwargs: same as tf.get_variable()
-        """
-        with tf.variable_scope(self._name) as scope:
-            with tf.variable_scope(scope, reuse=False):
-                scope = tf.get_variable_scope()
-                assert scope.reuse == False
-                return tf.get_variable(*args, **kwargs)
-
-    def find_tensor_in_main_tower(self, graph, name):
-        if self.is_main_tower:
-            return graph.get_tensor_by_name(name)
-        if name.startswith('towerp'):
-            newname = re.sub('towerp[0-9]+/', '', name)
-            try:
-                return graph.get_tensor_by_name(newname)
-            except KeyError:
-                newname = re.sub('towerp[0-9]+/', 'tower0/', name)
-                return graph.get_tensor_by_name(newname)
-
-    def __enter__(self):
-        global _CurrentTowerContext
-        assert _CurrentTowerContext is None, \
-                "Nesting TowerContext!"
-        _CurrentTowerContext = self
-        if len(self._name):
-            self._scope = tf.name_scope(self._name)
-            return self._scope.__enter__()
-
-    def __exit__(self, exc_type, exc_val, exc_tb):
-        global _CurrentTowerContext
-        _CurrentTowerContext = None
-        if len(self._name):
-            self._scope.__exit__(exc_type, exc_val, exc_tb)
-        return False
-
-def get_current_tower_context():
-    global _CurrentTowerContext
-    return _CurrentTowerContext
-
 class ModelDesc(object):
     """ Base class for a model description """
     __metaclass__ = ABCMeta

tensorpack/models/nonlin.py

@@ -6,7 +6,7 @@
 import tensorflow as tf
 from copy import copy
 
-from ._common import *
+from ._common import layer_register
 from .batch_norm import BatchNorm
 
 __all__ = ['Maxout', 'PReLU', 'LeakyReLU', 'BNReLU']

tensorpack/models/pool.py

@@ -5,8 +5,8 @@
 import tensorflow as tf
 import numpy
 
-from ._common import *
-from ..tfutils.symbolic_functions import *
+from ._common import layer_register, shape2d, shape4d
+from ..tfutils import symbolic_functions as symbf
 
 __all__ = ['MaxPooling', 'FixedUnPooling', 'AvgPooling', 'GlobalAvgPooling',
            'BilinearUpSample']
@@ -105,9 +105,9 @@ def FixedUnPooling(x, shape, unpool_mat=None):
     assert unpool_mat.get_shape().as_list() == list(shape)
 
     # perform a tensor-matrix kronecker product
-    fx = flatten(tf.transpose(x, [0, 3, 1, 2]))
+    fx = symbf.flatten(tf.transpose(x, [0, 3, 1, 2]))
     fx = tf.expand_dims(fx, -1)       # (bchw)x1
-    mat = tf.expand_dims(flatten(unpool_mat), 0)    #1x(shxsw)
+    mat = tf.expand_dims(symbf.flatten(unpool_mat), 0)    #1x(shxsw)
     prod = tf.matmul(fx, mat)    #(bchw) x(shxsw)
     prod = tf.reshape(prod, tf.pack(
         [-1, input_shape[3], input_shape[1], input_shape[2], shape[0], shape[1]]))

tensorpack/models/regularize.py

@@ -6,8 +6,8 @@ import tensorflow as tf
 import re
 
 from ..utils import logger
-from ..utils.utils import *
-from .model_desc import get_current_tower_context
+from ..utils.utils import memoized
+from ..tfutils.tower import get_current_tower_context
 from ._common import layer_register
 
 __all__ = ['regularize_cost', 'l2_regularizer', 'l1_regularizer', 'Dropout']

tensorpack/predict/base.py

@@ -7,9 +7,8 @@ from abc import abstractmethod, ABCMeta, abstractproperty
 import tensorflow as tf
 import six
 
-from ..models import TowerContext
 from ..utils import logger
-from ..tfutils import get_vars_by_names
+from ..tfutils import get_vars_by_names, TowerContext
 
 __all__ = ['OnlinePredictor', 'OfflinePredictor',
         'AsyncPredictorBase',

tensorpack/predict/common.py

@@ -9,7 +9,8 @@ from six.moves import zip
 
 from tensorpack.models import ModelDesc
 from ..utils import logger
-from ..tfutils import *
+from ..tfutils import get_default_sess_config
+from ..tfutils.sessinit import SessionInit, JustCurrentSession
 from .base import OfflinePredictor
 
 import multiprocessing

tensorpack/predict/concurrency.py

@@ -9,12 +9,9 @@ import time
 import six
 from six.moves import queue, range, zip
 
-
 from ..utils.concurrency import DIE
 from ..tfutils.modelutils import describe_model
 from ..utils import logger
-from ..utils.timer import *
-from ..tfutils import *
 
 from .base import *
 

tensorpack/tfutils/__init__.py

@@ -17,4 +17,5 @@ _global_import('sessinit')
 _global_import('common')
 _global_import('gradproc')
 _global_import('argscope')
+_global_import('tower')
 

tensorpack/tfutils/modelutils.py

@@ -6,6 +6,8 @@ import tensorflow as tf
 
 from ..utils import logger
 
+__all__ = ['describe_model', 'get_shape_str']
+
 def describe_model():
     """ print a description of the current model parameters """
     train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)

tensorpack/tfutils/summary.py

@@ -7,6 +7,7 @@ import tensorflow as tf
 import re
 
 from ..utils import *
+from .tower import get_current_tower_context
 from . import get_global_step_var
 from .symbolic_functions import rms
 
@@ -28,6 +29,8 @@ def add_activation_summary(x, name=None):
     Add summary to graph for an activation tensor x.
     If name is None, use x.name.
     """
+    if not get_current_tower_context().is_main_training_tower:
+        return
     ndim = x.get_shape().ndims
     assert ndim >= 2, \
         "Summary a scalar with histogram? Maybe use scalar instead. FIXME!"
@@ -46,6 +49,8 @@ def add_param_summary(summary_lists):
     :param summary_lists: list of (regex, [list of summary type to perform]).
         Type can be 'mean', 'scalar', 'histogram', 'sparsity', 'rms'
     """
+    if not get_current_tower_context().is_main_training_tower:
+        return
     def perform(var, action):
         ndim = var.get_shape().ndims
         name = var.name.replace(':0', '')
@@ -84,6 +89,8 @@ def add_moving_summary(v, *args):
     :param v: tensor or list of tensor to summary
     :param args: tensors to summary
     """
+    if not get_current_tower_context().is_main_training_tower:
+        return
     if not isinstance(v, list):
         v = [v]
     v.extend(args)

tensorpack/tfutils/tower.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# File: tower.py
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
+
+import tensorflow as tf
+
+__all__ = ['get_current_tower_context', 'TowerContext']
+
+_CurrentTowerContext = None
+
+class TowerContext(object):
+    def __init__(self, tower_name, is_training=None):
+        """ tower_name: 'tower0', 'towerp0', or '' """
+        self._name = tower_name
+        if is_training is None:
+            is_training = not self._name.startswith('towerp')
+        self._is_training = is_training
+
+    @property
+    def is_main_training_tower(self):
+        return self.is_training and (self._name == '' or self._name == 'tower0')
+
+    @property
+    def is_main_tower(self):
+        return self._name == '' or self._name == 'tower0'
+
+    @property
+    def is_training(self):
+        return self._is_training
+
+    @property
+    def name(self):
+        return self._name
+
+    def get_variable_on_tower(self, *args, **kwargs):
+        """
+        Get a variable for this tower specifically, without reusing.
+        Tensorflow doesn't allow reuse=False scope under a
+        reuse=True scope. This method provides a work around.
+        See https://www.tensorflow.org/versions/master/how_tos/variable_scope/index.html#basics-of-tfvariable-scope
+
+        :param args, kwargs: same as tf.get_variable()
+        """
+        with tf.variable_scope(self._name) as scope:
+            with tf.variable_scope(scope, reuse=False):
+                scope = tf.get_variable_scope()
+                assert scope.reuse == False
+                return tf.get_variable(*args, **kwargs)
+
+    def find_tensor_in_main_tower(self, graph, name):
+        if self.is_main_tower:
+            return graph.get_tensor_by_name(name)
+        if name.startswith('towerp'):
+            newname = re.sub('towerp[0-9]+/', '', name)
+            try:
+                return graph.get_tensor_by_name(newname)
+            except KeyError:
+                newname = re.sub('towerp[0-9]+/', 'tower0/', name)
+                return graph.get_tensor_by_name(newname)
+
+    def __enter__(self):
+        global _CurrentTowerContext
+        assert _CurrentTowerContext is None, \
+                "Nesting TowerContext!"
+        _CurrentTowerContext = self
+        if len(self._name):
+            self._scope = tf.name_scope(self._name)
+            return self._scope.__enter__()
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        global _CurrentTowerContext
+        _CurrentTowerContext = None
+        if len(self._name):
+            self._scope.__exit__(exc_type, exc_val, exc_tb)
+        return False
+
+def get_current_tower_context():
+    global _CurrentTowerContext
+    return _CurrentTowerContext
+

tensorpack/train/multigpu.py

@@ -7,14 +7,13 @@ import tensorflow as tf
 import itertools, re
 from six.moves import zip, range
 
-from ..models import TowerContext
 from ..utils import logger
 from ..utils.naming import *
 from ..utils.concurrency import LoopThread
 from ..tfutils.summary import summary_moving_average
 from ..tfutils.modelutils import describe_model
 from ..tfutils import (backup_collection, restore_collection,
-        get_global_step_var)
+        get_global_step_var, TowerContext)
 
 from .trainer import QueueInputTrainer
 

tensorpack/train/trainer.py

@@ -11,10 +11,9 @@ from .base import Trainer
 
 from ..dataflow.common import RepeatedData
 
-from ..models import TowerContext
 from ..utils import logger, SUMMARY_BACKUP_KEYS
 from ..tfutils import (get_vars_by_names, freeze_collection,
-        get_global_step_var)
+        get_global_step_var, TowerContext)
 from ..tfutils.summary import summary_moving_average, add_moving_summary
 from ..tfutils.modelutils import describe_model
 from ..predict import OnlinePredictor, build_multi_tower_prediction_graph
@@ -67,7 +66,7 @@ class SimpleTrainer(Trainer):
         with TowerContext(''):
             model.build_graph(self.input_vars)
             cost_var = model.get_cost() # TODO assert scalar
-        add_moving_summary(cost_var)
+            add_moving_summary(cost_var)
 
         grads = self.config.optimizer.compute_gradients(cost_var)
         grads = self.process_grads(grads)