more consistent with official cifar code

example_cifar10.py

@@ -19,7 +19,7 @@ from tensorpack.dataflow import *
 from tensorpack.dataflow import imgaug
 
 BATCH_SIZE = 128
-MIN_AFTER_DEQUEUE = 500
+MIN_AFTER_DEQUEUE = 20000   # a large number, as in the official example
 CAPACITY = MIN_AFTER_DEQUEUE + 3 * BATCH_SIZE
 
 def get_model(inputs, is_training):
@@ -28,7 +28,10 @@ def get_model(inputs, is_training):
 
     image, label = inputs
 
-    #if is_training:    # slow
+    if is_training:    # slow?
+        image, label = tf.train.shuffle_batch(
+            [image, label], BATCH_SIZE, CAPACITY, MIN_AFTER_DEQUEUE,
+            num_threads=6, enqueue_many=False)
         ## augmentations
         #image, label = tf.train.slice_input_producer(
             #[image, label], name='slice_queue')
@@ -80,20 +83,23 @@ def get_config():
     log_dir = os.path.join('train_log', os.path.basename(__file__)[:-3])
     logger.set_logger_dir(log_dir)
 
-    import cv2
     dataset_train = dataset.Cifar10('train')
-    augmentor = imgaug.AugmentorList([
+    augmentors = [
         RandomCrop((24, 24)),
         Flip(horiz=True),
         BrightnessAdd(0.25),
         Contrast((0.2,1.8)),
         PerImageWhitening()
-    ])
-    dataset_train = MapData(dataset_train, lambda img:
-                            augmentor.augment(imgaug.Image(img)).arr)
+    ]
+    dataset_train = AugmentImageComponent(dataset_train, augmentors)
     dataset_train = BatchData(dataset_train, 128)
+
+    augmentors = [
+        CenterCrop((24, 24)),
+        PerImageWhitening()
+    ]
     dataset_test = dataset.Cifar10('test')
-    dataset_test = MapData(dataset_test, lambda img: cv2.resize(img, (24, 24)))
+    dataset_test = AugmentImageComponent(dataset_test, augmentors)
     dataset_test = BatchData(dataset_test, 128)
     step_per_epoch = dataset_train.size()
     #step_per_epoch = 20
@@ -109,19 +115,19 @@ def get_config():
         tf.placeholder(
             tf.int32, shape=(None,), name='label')
     ]
-    input_queue = tf.RandomShuffleQueue(
-        100, 50, [x.dtype for x in input_vars], name='queue')
+    input_queue = tf.FIFOQueue(
+        50, [x.dtype for x in input_vars], name='queue')
 
     lr = tf.train.exponential_decay(
-        learning_rate=1e-4,
+        learning_rate=1e-1,
         global_step=get_global_step_var(),
-        decay_steps=dataset_train.size() * 50,
+        decay_steps=dataset_train.size() * 200,
         decay_rate=0.1, staircase=True, name='learning_rate')
     tf.scalar_summary('learning_rate', lr)
 
     return TrainConfig(
         dataset=dataset_train,
-        optimizer=tf.train.AdamOptimizer(lr),
+        optimizer=tf.train.GradientDescentOptimizer(lr),
         callbacks=Callbacks([
             SummaryWriter(),
             PeriodicSaver(),
@@ -131,6 +137,7 @@ def get_config():
         inputs=input_vars,
         input_queue=input_queue,
         get_model_func=get_model,
+        batched_model_input=False,
         step_per_epoch=step_per_epoch,
         max_epoch=100,
     )

example_mnist.py

@@ -42,13 +42,12 @@ def get_model(inputs, is_training):
     image, label = inputs
     image = tf.expand_dims(image, 3)    # add a single channel
 
-    #if is_training:    # slow
+    #if is_training:
         ## augmentations
         #image, label = tf.train.slice_input_producer(
-            #[image, label], name='slice_queue')
-        #image = tf.image.random_brightness(image, 0.1)
-        #image, label = tf.train.shuffle_batch(
-            #[image, label], BATCH_SIZE, CAPACITY, MIN_AFTER_DEQUEUE,
+            #[image, label], shuffle=False, name='slice_queue')
+        #image, label = tf.train.batch(
+            #[image, label], BATCH_SIZE, capacity=CAPACITY,
             #num_threads=2, enqueue_many=False)
 
     l = Conv2D('conv0', image, out_channel=32, kernel_shape=5)
@@ -98,7 +97,7 @@ def get_config():
     dataset_train = BatchData(dataset.Mnist('train'), 128)
     dataset_test = BatchData(dataset.Mnist('test'), 256, remainder=True)
     step_per_epoch = dataset_train.size()
-    step_per_epoch = 2
+    #step_per_epoch = 30
     #dataset_test = FixedSizeData(dataset_test, 20)
 
     sess_config = get_default_sess_config()

tensorpack/dataflow/common.py

@@ -5,8 +5,10 @@
 
 import numpy as np
 from .base import DataFlow
+from imgaug import AugmentorList, Image
 
-__all__ = ['BatchData', 'FixedSizeData', 'FakeData', 'MapData']
+__all__ = ['BatchData', 'FixedSizeData', 'FakeData', 'MapData',
+           'AugmentImageComponent']
 
 class BatchData(DataFlow):
     def __init__(self, ds, batch_size, remainder=False):
@@ -101,3 +103,17 @@ class MapData(DataFlow):
             d = list(dp)
             dp[self.index] = self.func(dp[self.index])
             yield dp
+
+def AugmentImageComponent(ds, augmentors, index=0):
+    """
+    Augment the image in each data point
+    Args:
+        ds: a DataFlow dataset instance
+        augmentors: a list of ImageAugmentor instance
+        index: the index of image in each data point. default to be 0
+    """
+    aug = AugmentorList(augmentors)
+    return MapData(
+        ds,
+        lambda img: aug.augment(Image(img)).arr,
+        index)

tensorpack/dataflow/imgaug/base.py

@@ -60,6 +60,7 @@ class AugmentorList(ImageAugmentor):
         self.augs = augmentors
 
     def _augment(self, img):
+        assert img.arr.ndim in [2, 3]
         img.arr = img.arr.astype('float32') / 255.0
         for aug in self.augs:
             aug.augment(img)

tensorpack/dataflow/imgaug/crop.py

@@ -5,12 +5,12 @@
 
 from .base import ImageAugmentor
 
-__all__ = ['RandomCrop']
+__all__ = ['RandomCrop', 'CenterCrop', 'Resize']
 
 class RandomCrop(ImageAugmentor):
+    """ Randomly crop the image into a smaller one """
     def __init__(self, crop_shape):
         """
-        Randomly crop the image into a smaller one
         Args:
             crop_shape: shape in (h, w)
         """
@@ -24,4 +24,29 @@ class RandomCrop(ImageAugmentor):
         if img.coords:
             raise NotImplementedError()
 
+class CenterCrop(ImageAugmentor):
+    """ Crop the image in the center"""
+    def __init__(self, crop_shape):
+        self._init(locals())
+
+    def _augment(self, img):
+        orig_shape = img.arr.shape
+        h0 = (orig_shape[0] - self.crop_shape[0]) * 0.5
+        w0 = (orig_shape[1] - self.crop_shape[1]) * 0.5
+        img.arr = img.arr[h0:h0+self.crop_shape[0],w0:w0+self.crop_shape[1]]
+        if img.coords:
+            raise NotImplementedError()
 
+class Resize(ImageAugmentor):
+    """Resize image to a target size"""
+    def __init__(self, shape):
+        """
+        Args:
+            shape: (w, h)
+        """
+        self._init(locals())
+
+    def _augment(self, img):
+        img.arr = cv2.resize(
+            img.arr, self.shape,
+            interpolation=cv2.INTER_CUBIC)

tensorpack/dataflow/imgaug/imgproc.py

@@ -33,12 +33,13 @@ class Contrast(ImageAugmentor):
         r = self._rand_range(*self.factor_range)
         mean = np.mean(arr, axis=(0,1), keepdims=True)
         img.arr = (arr - mean) * r + mean
+        img.arr = np.clip(img.arr, 0, 1)
 
 class PerImageWhitening(ImageAugmentor):
     """
     Linearly scales image to have zero mean and unit norm.
     x = (x - mean) / adjusted_stddev
-    where adjusted_stddev = max(stddev, 1.0/sqrt(num_pixels))
+    where adjusted_stddev = max(stddev, 1.0/sqrt(num_pixels * channels))
     """
     def __init__(self):
         pass
@@ -46,5 +47,5 @@ class PerImageWhitening(ImageAugmentor):
     def _augment(self, img):
         mean = np.mean(img.arr, axis=(0,1), keepdims=True)
         std = np.std(img.arr, axis=(0,1), keepdims=True)
-        std = np.maximum(std, 1.0 / np.sqrt(np.prod(img.arr.shape[:2])))
+        std = np.maximum(std, 1.0 / np.sqrt(np.prod(img.arr.shape)))
         img.arr = (img.arr - mean) / std

tensorpack/train.py

@@ -36,6 +36,11 @@ class TrainConfig(object):
                 with capacity 5
             get_model_func: a function taking `inputs` and `is_training` and
                 return a tuple of output list as well as the cost to minimize
+            batched_model_input: boolean. If yes, `get_model_func` expected batched
+                input in training. Otherwise, expect single data point in
+                training, so that you may do pre-processing and batch them
+                later with batch ops. It's suggested that you do all
+                preprocessing in dataset as that is usually faster.
             step_per_epoch: the number of steps (parameter updates) to perform
                 in each epoch. default to dataset.size()
             max_epoch: maximum number of epoch to run training. default to 100
@@ -59,6 +64,7 @@ class TrainConfig(object):
         assert_type(self.input_queue, tf.QueueBase)
         assert self.input_queue.dtypes == [x.dtype for x in self.inputs]
         self.get_model_func = kwargs.pop('get_model_func')
+        self.batched_model_input = kwargs.pop('batched_model_input', True)
         self.step_per_epoch = int(kwargs.pop('step_per_epoch', self.dataset.size()))
         self.max_epoch = int(kwargs.pop('max_epoch', 100))
         assert self.step_per_epoch > 0 and self.max_epoch > 0
@@ -89,11 +95,16 @@ def start_train(config):
     input_queue = config.input_queue
     callbacks = config.callbacks
 
-    enqueue_op = input_queue.enqueue(tuple(input_vars))
+    if config.batched_model_input:
+        enqueue_op = input_queue.enqueue(input_vars)
         model_inputs = input_queue.dequeue()
-    # set dequeue shape
         for qv, v in zip(model_inputs, input_vars):
             qv.set_shape(v.get_shape())
+    else:
+        enqueue_op = input_queue.enqueue_many(input_vars)
+        model_inputs = input_queue.dequeue()
+        for qv, v in zip(model_inputs, input_vars):
+            qv.set_shape(v.get_shape().as_list()[1:])
     output_vars, cost_var = config.get_model_func(model_inputs, is_training=True)
 
     # build graph
@@ -125,6 +136,8 @@ def start_train(config):
                 for step in xrange(config.step_per_epoch):
                     if coord.should_stop():
                         return
+                    # TODO if no one uses trigger_step, train_op can be
+                    # faster, see: https://github.com/soumith/convnet-benchmarks/pull/67/files
                     fetches = [train_op, cost_var] + output_vars + model_inputs
                     results = sess.run(fetches)
                     cost = results[1]

tensorpack/utils/callback.py

@@ -111,9 +111,9 @@ class CallbackTimeLogger(object):
         msgs = []
         for name, t in self.times:
             if t / self.tot > 0.3 and t > 1:
-                msgs.append("{}:{}".format(name, t))
+                msgs.append("{}:{}sec".format(name, t))
         logger.info(
-            "Callbacks took {} sec. {}".format(
+            "Callbacks took {} sec in total. {}".format(
                 self.tot, ' '.join(msgs)))