update docs & small changes

examples/DeepQNetwork/common.py

@@ -87,6 +87,7 @@ def eval_with_funcs(predictors, nr_eval, get_player_fn, verbose=False):
 
     for _ in tqdm(range(nr_eval), **get_tqdm_kwargs()):
         fetch()
+    # waiting is necessary, otherwise the estimated mean score is biased
     logger.info("Waiting for all the workers to finish the last run...")
     for k in threads:
         k.stop()

examples/FasterRCNN/basemodel.py

@@ -26,7 +26,7 @@ def maybe_freeze_affine(getter, *args, **kwargs):
 def resnet_argscope():
     with argscope([Conv2D, MaxPooling, BatchNorm], data_format='channels_first'), \
             argscope(Conv2D, use_bias=False), \
-            argscope(BatchNorm, training=False, epsilon=0), \
+            argscope(BatchNorm, training=False), \
             custom_getter_scope(maybe_freeze_affine):
         yield
 

examples/ImageNetModels/imagenet_utils.py

@@ -150,8 +150,10 @@ class ImageNetModel(ModelDesc):
     """
     weight_decay_on_bn = False
 
-    def __init__(self, data_format='NCHW'):
-        self.data_format = data_format
+    """
+    Either 'NCHW' or 'NHWC'
+    """
+    data_format = 'NCHW'
 
     def inputs(self):
         return [tf.placeholder(self.image_dtype, [None, self.image_shape, self.image_shape, 3], 'input'),

examples/ResNet/imagenet-resnet.py

@@ -25,9 +25,7 @@ from resnet_model import (
 
 
 class Model(ImageNetModel):
-    def __init__(self, depth, data_format='NCHW', mode='resnet'):
-        super(Model, self).__init__(data_format)
-
+    def __init__(self, depth, mode='resnet'):
         if mode == 'se':
             assert depth >= 50
 
@@ -64,17 +62,17 @@ def get_config(model, fake=False):
     assert args.batch % nr_tower == 0
     batch = args.batch // nr_tower
 
+    logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))
     if fake:
-        logger.info("For benchmark, batch size is fixed to 64 per tower.")
         dataset_train = FakeData(
-            [[64, 224, 224, 3], [64]], 1000, random=False, dtype='uint8')
+            [[batch, 224, 224, 3], [batch]], 1000, random=False, dtype='uint8')
         callbacks = []
     else:
-        logger.info("Running on {} towers. Batch size per tower: {}".format(nr_tower, batch))
         dataset_train = get_data('train', batch)
         dataset_val = get_data('val', batch)
 
-        BASE_LR = 0.1 * (args.batch / 256.0)
+        START_LR = 0.1
+        BASE_LR = START_LR * (args.batch / 256.0)
         callbacks = [
             ModelSaver(),
             EstimatedTimeLeft(),
@@ -82,10 +80,10 @@ def get_config(model, fake=False):
                 'learning_rate', [(30, BASE_LR * 1e-1), (60, BASE_LR * 1e-2),
                                   (90, BASE_LR * 1e-3), (100, BASE_LR * 1e-4)]),
         ]
-        if BASE_LR > 0.1:
+        if BASE_LR > START_LR:
             callbacks.append(
                 ScheduledHyperParamSetter(
-                    'learning_rate', [(0, 0.1), (3, BASE_LR)], interp='linear'))
+                    'learning_rate', [(0, START_LR), (5, BASE_LR)], interp='linear'))
 
         infs = [ClassificationError('wrong-top1', 'val-error-top1'),
                 ClassificationError('wrong-top5', 'val-error-top5')]
@@ -126,7 +124,8 @@ if __name__ == '__main__':
     if args.gpu:
         os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
 
-    model = Model(args.depth, args.data_format, args.mode)
+    model = Model(args.depth, args.mode)
+    model.data_format = args.data_format
     if args.eval:
         batch = 128    # something that can run on one gpu
         ds = get_data('val', batch)

examples/Saliency/README.md

@@ -39,7 +39,7 @@ Usage:
 ./CAM-resnet.py --data /path/to/imagenet [--load ImageNet-ResNet18-Preact.npz] [--gpu 0,1,2,3]
 ```
 Pretrained and fine-tuned ResNet can be downloaded
-[here](http://models.tensorpack.com/ResNet/) and [here](http://models.tensorpack.com/Visualization/).
+in the [model zoo](http://models.tensorpack.com/).
 
 2. Generate CAM on ImageNet validation set:
 ```bash

tensorpack/dataflow/common.py

@@ -301,7 +301,7 @@ class MapDataComponent(MapData):
             r = func(dp[index])
             if r is None:
                 return None
-            dp = copy(dp)   # shallow copy to avoid modifying the list
+            dp = list(dp)   # shallow copy to avoid modifying the list
             dp[index] = r
             return dp
         super(MapDataComponent, self).__init__(ds, f)
@@ -606,6 +606,9 @@ class CacheData(ProxyDataFlow):
     """
     Cache the first pass of a DataFlow completely in memory,
     and produce from the cache thereafter.
+
+    NOTE: The user should not stop the iterator before it has reached the end.
+        Otherwise the cache may be incomplete.
     """
     def __init__(self, ds, shuffle=False):
         """

tensorpack/dataflow/imgaug/imgproc.py

@@ -268,7 +268,8 @@ class Lighting(ImageAugmentor):
 
     def _get_augment_params(self, img):
         assert img.shape[2] == 3
-        return self.rng.randn(3) * self.std
+        ret = self.rng.randn(3) * self.std
+        return ret.astype('float32')
 
     def _augment(self, img, v):
         old_dtype = img.dtype

tensorpack/dataflow/parallel.py

@@ -403,12 +403,13 @@ class PlasmaPutData(ProxyDataFlow):
 
     Experimental.
     """
-    def __init__(self, ds):
+    def __init__(self, ds, socket="/tmp/plasma"):
+        self._socket = socket
         super(PlasmaPutData, self).__init__(ds)
 
     def reset_state(self):
         super(PlasmaPutData, self).reset_state()
-        self.client = plasma.connect("/tmp/plasma", "", 0)
+        self.client = plasma.connect(self._socket, "", 0)
 
     def get_data(self):
         for dp in self.ds.get_data():
@@ -421,12 +422,13 @@ class PlasmaGetData(ProxyDataFlow):
     Take plasma object id from a DataFlow, and retrieve it from plasma shared
     memory object store.
     """
-    def __init__(self, ds):
+    def __init__(self, ds, socket="/tmp/plasma"):
+        self._socket = socket
         super(PlasmaGetData, self).__init__(ds)
 
     def reset_state(self):
         super(PlasmaGetData, self).reset_state()
-        self.client = plasma.connect("/tmp/plasma", "", 0)
+        self.client = plasma.connect(self._socket, "", 0)
 
     def get_data(self):
         for dp in self.ds.get_data():

tensorpack/tfutils/common.py

@@ -38,8 +38,10 @@ def get_default_sess_config(mem_fraction=0.99):
     # Didn't see much difference.
 
     conf.gpu_options.per_process_gpu_memory_fraction = 0.99
-    if get_tf_version_number() >= 1.2:
-        conf.gpu_options.force_gpu_compatible = True
+
+    # This hurt performance of large data pipeline:
+    # https://github.com/tensorflow/benchmarks/commit/1528c46499cdcff669b5d7c006b7b971884ad0e6
+    # conf.gpu_options.force_gpu_compatible = True
 
     conf.gpu_options.allow_growth = True
 
@@ -47,7 +49,7 @@ def get_default_sess_config(mem_fraction=0.99):
     # conf.graph_options.rewrite_options.memory_optimization = \
     #     rwc.RewriterConfig.HEURISTICS
 
-    # May hurt performance
+    # May hurt performance?
     # conf.graph_options.optimizer_options.global_jit_level = tf.OptimizerOptions.ON_1
     # conf.graph_options.place_pruned_graph = True
     return conf