split-out callbacks dir

example_cifar10.py

@@ -13,8 +13,7 @@ from tensorpack.models import *
 from tensorpack.utils import *
 from tensorpack.utils.symbolic_functions import *
 from tensorpack.utils.summary import *
-from tensorpack.utils.callback import *
-from tensorpack.utils.validation_callback import *
+from tensorpack.callbacks import *
 from tensorpack.dataflow import *
 from tensorpack.dataflow import imgaug
 

example_mnist.py

@@ -15,8 +15,7 @@ from tensorpack.models import *
 from tensorpack.utils import *
 from tensorpack.utils.symbolic_functions import *
 from tensorpack.utils.summary import *
-from tensorpack.utils.callback import *
-from tensorpack.utils.validation_callback import *
+from tensorpack.callbacks import *
 from tensorpack.dataflow import *
 
 BATCH_SIZE = 128

tensorpack/callbacks/__init__.py

+# !/usr/bin/env python2
+#  -*- coding: UTF-8 -*-
+#  File: __init__.py
+#  Author: Yuxin Wu <ppwwyyxx@gmail.com>
+
+from pkgutil import walk_packages
+import os
+
+def global_import(name):
+    p = __import__(name, globals(), locals())
+    lst = p.__all__ if '__all__' in dir(p) else dir(p)
+    for k in lst:
+        globals()[k] = p.__dict__[k]
+
+for _, module_name, _ in walk_packages(
+        [os.path.dirname(__file__)]):
+    if not module_name.startswith('_'):
+        global_import(module_name)
+

tensorpack/callbacks/base.py

+#!/usr/bin/env python2
+# -*- coding: UTF-8 -*-
+# File: base.py
+# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+
+import tensorflow as tf
+import sys
+import os
+import time
+from abc import abstractmethod, ABCMeta
+
+from ..utils import *
+
+__all__ = ['Callback', 'PeriodicCallback']
+
+class Callback(object):
+    __metaclass__ = ABCMeta
+
+    running_graph = 'train'
+    """ The graph that this callback should run on.
+        Either 'train' or 'test'
+    """
+
+    def before_train(self):
+        self.graph = tf.get_default_graph()
+        self.sess = tf.get_default_session()
+        self._before_train()
+
+    def _before_train(self):
+        """
+        Called before starting iterative training
+        """
+
+    def trigger_step(self, inputs, outputs, cost):
+        """
+        Callback to be triggered after every step (every backpropagation)
+        Args:
+            inputs: the list of input values
+            outputs: list of output values after running this inputs
+            cost: the cost value after running this input
+        """
+
+    def trigger_epoch(self):
+        """
+        Callback to be triggered after every epoch (full iteration of input dataset)
+        """
+
+class PeriodicCallback(Callback):
+    def __init__(self, period):
+        self.__period = period
+        self.epoch_num = 0
+
+    def trigger_epoch(self):
+        self.epoch_num += 1
+        if self.epoch_num % self.__period == 0:
+            self.global_step = get_global_step()
+            self._trigger()
+
+    @abstractmethod
+    def _trigger(self):
+        pass
+

tensorpack/callbacks/common.py

+#!/usr/bin/env python2
+# -*- coding: UTF-8 -*-
+# File: common.py
+# Author: Yuxin Wu <ppwwyyxx@gmail.com>
+
+import tensorflow as tf
+import os
+
+from .base import Callback, PeriodicCallback
+from ..utils import *
+
+__all__ = ['PeriodicSaver', 'SummaryWriter']
+
+class PeriodicSaver(PeriodicCallback):
+    def __init__(self, period=1, keep_recent=10, keep_freq=0.5):
+        super(PeriodicSaver, self).__init__(period)
+        self.path = os.path.join(logger.LOG_DIR, 'model')
+        self.keep_recent = keep_recent
+        self.keep_freq = keep_freq
+
+    def _before_train(self):
+        self.saver = tf.train.Saver(
+            max_to_keep=self.keep_recent,
+            keep_checkpoint_every_n_hours=self.keep_freq)
+
+    def _trigger(self):
+        self.saver.save(
+            tf.get_default_session(),
+            self.path,
+            global_step=self.global_step)
+
+class SummaryWriter(Callback):
+    def __init__(self):
+        self.log_dir = logger.LOG_DIR
+
+    def _before_train(self):
+        self.writer = tf.train.SummaryWriter(
+            self.log_dir, graph_def=self.sess.graph_def)
+        tf.add_to_collection(SUMMARY_WRITER_COLLECTION_KEY, self.writer)
+        self.summary_op = tf.merge_all_summaries()
+
+    def trigger_epoch(self):
+        # check if there is any summary
+        if self.summary_op is None:
+            return
+        summary_str = self.summary_op.eval()
+        self.writer.add_summary(summary_str, get_global_step())
+

tensorpack/utils/callback.py → tensorpack/callbacks/group.py

 #!/usr/bin/env python2
 # -*- coding: UTF-8 -*-
-# File: callback.py
+# File: group.py
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 
 import tensorflow as tf
-import sys
-import numpy as np
-import os
-import time
-from abc import abstractmethod, ABCMeta
-
-from . import create_test_session, get_global_step
-from .naming import *
-import logger
-
-class Callback(object):
-    __metaclass__ = ABCMeta
-    running_graph = 'train'
-    """ The graph that this callback should run on.
-        Either 'train' or 'test'
-    """
-
-    def before_train(self):
-        self.graph = tf.get_default_graph()
-        self.sess = tf.get_default_session()
-        self._before_train()
-
-    def _before_train(self):
-        """
-        Called before starting iterative training
-        """
-
-    def trigger_step(self, inputs, outputs, cost):
-        """
-        Callback to be triggered after every step (every backpropagation)
-        Args:
-            inputs: the list of input values
-            outputs: list of output values after running this inputs
-            cost: the cost value after running this input
-        """
-
-    def trigger_epoch(self):
-        """
-        Callback to be triggered after every epoch (full iteration of input dataset)
-        """
-
-class PeriodicCallback(Callback):
-    def __init__(self, period):
-        self.__period = period
-        self.epoch_num = 0
-
-    def trigger_epoch(self):
-        self.epoch_num += 1
-        if self.epoch_num % self.__period == 0:
-            self.global_step = get_global_step()
-            self._trigger()
-
-    @abstractmethod
-    def _trigger(self):
-        pass
-
-class PeriodicSaver(PeriodicCallback):
-    def __init__(self, period=1, keep_recent=10, keep_freq=0.5):
-        super(PeriodicSaver, self).__init__(period)
-        self.path = os.path.join(logger.LOG_DIR, 'model')
-        self.keep_recent = keep_recent
-        self.keep_freq = keep_freq
-
-    def _before_train(self):
-        self.saver = tf.train.Saver(
-            max_to_keep=self.keep_recent,
-            keep_checkpoint_every_n_hours=self.keep_freq)
-
-    def _trigger(self):
-        self.saver.save(
-            tf.get_default_session(),
-            self.path,
-            global_step=self.global_step)
-
-class SummaryWriter(Callback):
-    def __init__(self):
-        self.log_dir = logger.LOG_DIR
-
-    def _before_train(self):
-        self.writer = tf.train.SummaryWriter(
-            self.log_dir, graph_def=self.sess.graph_def)
-        tf.add_to_collection(SUMMARY_WRITER_COLLECTION_KEY, self.writer)
-        self.summary_op = tf.merge_all_summaries()
-
-    def trigger_epoch(self):
-        # check if there is any summary
-        if self.summary_op is None:
-            return
-        summary_str = self.summary_op.eval()
-        self.writer.add_summary(summary_str, get_global_step())
+from contextlib import contextmanager
+
+from .base import Callback
+from .common import *
+from ..utils import *
+
+__all__ = ['Callbacks']
+
+@contextmanager
+def create_test_graph():
+    G = tf.get_default_graph()
+    input_vars_train = G.get_collection(INPUT_VARS_KEY)
+    forward_func = G.get_collection(FORWARD_FUNC_KEY)[0]
+    with tf.Graph().as_default() as Gtest:
+        # create a global step var in test graph
+        global_step_var = tf.Variable(
+            0, trainable=False, name=GLOBAL_STEP_OP_NAME)
+        input_vars = []
+        for v in input_vars_train:
+            name = v.name
+            assert name.endswith(':0'), "I think placeholder variable should all ends with ':0'"
+            name = name[:-2]
+            input_vars.append(tf.placeholder(
+                v.dtype, shape=v.get_shape(), name=name
+            ))
+        for v in input_vars:
+            Gtest.add_to_collection(INPUT_VARS_KEY, v)
+        output_vars, cost = forward_func(input_vars, is_training=False)
+        for v in output_vars:
+            Gtest.add_to_collection(OUTPUT_VARS_KEY, v)
+        yield Gtest
+
+@contextmanager
+def create_test_session():
+    with create_test_graph():
+        with tf.Session() as sess:
+            yield sess
 
 class CallbackTimeLogger(object):
     def __init__(self):
@@ -126,7 +73,7 @@ class TrainCallbacks(Callback):
                 self.cbs.insert(0, self.cbs.pop(idx))
                 break
         else:
-            raise RuntimeError("Callbacks must contain a SummaryWriter!")
+            raise ValueError("Callbacks must contain a SummaryWriter!")
 
     def before_train(self):
         for cb in self.cbs:
@@ -199,7 +146,7 @@ class Callbacks(Callback):
             elif cb.running_graph == 'train':
                 train_cbs.append(cb)
             else:
-                raise RuntimeError(
+                raise ValueError(
                     "Unknown callback running graph {}!".format(cb.running_graph))
         self.train = TrainCallbacks(train_cbs)
         self.test = TestCallbacks(test_cbs)
@@ -216,4 +163,3 @@ class Callbacks(Callback):
         self.train.trigger_epoch()
         # TODO test callbacks can be run async?
         self.test.trigger_epoch()
-

tensorpack/utils/validation_callback.py → tensorpack/callbacks/validation_callback.py

@@ -6,11 +6,12 @@
 import tensorflow as tf
 from tqdm import tqdm
 
-from .stat import *
-from .callback import PeriodicCallback, Callback
-from .naming import *
-from .summary import *
-import logger
+from ..utils import *
+from ..utils.stat import *
+from ..utils.summary import *
+from .base import PeriodicCallback, Callback
+
+__all__ = ['ValidationError']
 
 class ValidationError(PeriodicCallback):
     running_graph = 'test'

tensorpack/models/conv2d.py

@@ -18,7 +18,7 @@ def Conv2D(x, out_channel, kernel_shape,
     kernel_shape: (h, w) or a int
     stride: (h, w) or a int
     padding: 'valid' or 'same'
-    split: split channels. used in alexnet
+    split: split channels. used in Alexnet
     """
     in_shape = x.get_shape().as_list()
     in_channel = in_shape[-1]
@@ -31,7 +31,7 @@ def Conv2D(x, out_channel, kernel_shape,
     stride = shape4d(stride)
 
     if W_init is None:
-        W_init = tf.truncated_normal_initializer(stddev=1e-4)
+        W_init = tf.truncated_normal_initializer(stddev=4e-3)
     if b_init is None:
         b_init = tf.constant_initializer()
 

tensorpack/train.py

@@ -10,7 +10,7 @@ import argparse
 import tqdm
 from utils import *
 from utils.concurrency import EnqueueThread,coordinator_guard
-from utils.callback import Callbacks
+from callbacks import *
 from utils.summary import summary_moving_average
 from utils.modelutils import describe_model
 from utils import logger

tensorpack/utils/__init__.py

@@ -31,36 +31,6 @@ def timed_operation(msg, log_start=False):
     logger.info('finished {}, time={:.2f}sec.'.format(
         msg, time.time() - start))
 
-@contextmanager
-def create_test_graph():
-    G = tf.get_default_graph()
-    input_vars_train = G.get_collection(INPUT_VARS_KEY)
-    forward_func = G.get_collection(FORWARD_FUNC_KEY)[0]
-    with tf.Graph().as_default() as Gtest:
-        # create a global step var in test graph
-        global_step_var = tf.Variable(
-            0, trainable=False, name=GLOBAL_STEP_OP_NAME)
-        input_vars = []
-        for v in input_vars_train:
-            name = v.name
-            assert name.endswith(':0'), "I think placeholder variable should all ends with ':0'"
-            name = name[:-2]
-            input_vars.append(tf.placeholder(
-                v.dtype, shape=v.get_shape(), name=name
-            ))
-        for v in input_vars:
-            Gtest.add_to_collection(INPUT_VARS_KEY, v)
-        output_vars, cost = forward_func(input_vars, is_training=False)
-        for v in output_vars:
-            Gtest.add_to_collection(OUTPUT_VARS_KEY, v)
-        yield Gtest
-
-@contextmanager
-def create_test_session():
-    with create_test_graph():
-        with tf.Session() as sess:
-            yield sess
-
 def get_default_sess_config():
     """
     Return a better config to use as default.