suppress some prctl warnings

examples/Saliency/CAM-resnet.py

@@ -20,25 +20,18 @@ from tensorpack.utils.gpu import get_num_gpu
 from tensorpack.utils import viz
 
 from imagenet_utils import (
-    fbresnet_augmentor, image_preprocess, compute_loss_and_error)
+    fbresnet_augmentor, ImageNetModel)
 from resnet_model import (
     preresnet_basicblock, preresnet_group)
 
 
 TOTAL_BATCH_SIZE = 256
-INPUT_SHAPE = 224
 DEPTH = None
 
 
-class Model(ModelDesc):
-    def inputs(self):
-        return [tf.placeholder(tf.uint8, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
-                tf.placeholder(tf.int32, [None], 'label')]
-
-    def build_graph(self, image, label):
-        image = image_preprocess(image, bgr=True)
-        image = tf.transpose(image, [0, 3, 1, 2])
+class Model(ImageNetModel):
 
+    def get_logits(self, image):
         cfg = {
             18: ([2, 2, 2, 2], preresnet_basicblock),
             34: ([3, 4, 6, 3], preresnet_basicblock),
@@ -58,11 +51,7 @@ class Model(ModelDesc):
             print(convmaps)
             convmaps = GlobalAvgPooling('gap', convmaps)
             logits = FullyConnected('linearnew', convmaps, 1000)
-
-        loss = compute_loss_and_error(logits, label)
-        wd_cost = regularize_cost('.*/W', l2_regularizer(1e-4), name='l2_regularize_loss')
-        add_moving_summary(loss, wd_cost)
-        return tf.add_n([loss, wd_cost], name='cost')
+        return logits
 
     def optimizer(self):
         lr = tf.get_variable('learning_rate', initializer=0.1, trainable=False)
@@ -150,7 +139,7 @@ if __name__ == '__main__':
     parser.add_argument('--data', help='ILSVRC dataset dir')
     parser.add_argument('--depth', type=int, default=18)
     parser.add_argument('--load', help='load model')
-    parser.add_argument('--cam', action='store_true')
+    parser.add_argument('--cam', action='store_true', help='run visualization')
     args = parser.parse_args()
 
     DEPTH = args.depth

setup.py

@@ -28,7 +28,6 @@ setup(
         "termcolor>=1.1",
         "tabulate>=0.7.7",
         "tqdm>4.11.1",
-        "pyarrow>=0.9.0",
         "msgpack>=0.5.2",
         "msgpack-numpy>=0.4.0",
         "pyzmq>=16",

tensorpack/callbacks/param.py

@@ -363,5 +363,5 @@ class StatMonitorParamSetter(HyperParamSetter):
         self.last_changed_epoch = self.epoch_num
         logger.info(
             "[StatMonitorParamSetter] Triggered, history of {}: ".format(
-                self.stat_name) + ','.join(map(str, hist)))
+                self.stat_name) + ','.join([str(round(x, 3)) for x in hist]))
         return self.value_func(self.get_current_value())

tensorpack/dataflow/image.py

@@ -14,6 +14,7 @@ __all__ = ['ImageFromFile', 'AugmentImageComponent', 'AugmentImageCoordinates',
 
 
 def check_dtype(img):
+    assert isinstance(img, np.ndarray), "[Augmentor] Needs an numpy array, but got a {}!".format(type(img))
     if isinstance(img.dtype, np.integer):
         assert img.dtype == np.uint8, \
             "[Augmentor] Got image of type {}, use uint8 or floating points instead!".format(img.dtype)

tensorpack/dataflow/parallel.py

@@ -149,13 +149,14 @@ class MultiProcessPrefetchData(ProxyDataFlow):
     """
 
     class _Worker(mp.Process):
-        def __init__(self, ds, queue):
+        def __init__(self, ds, queue, idx):
             super(MultiProcessPrefetchData._Worker, self).__init__()
             self.ds = ds
             self.queue = queue
+            self.idx = idx
 
         def run(self):
-            enable_death_signal()
+            enable_death_signal(_warn=self.idx == 0)
             # reset all ds so each process will produce different data
             self.ds.reset_state()
             while True:
@@ -186,8 +187,8 @@ lead of failure on some of the code.")
                         "This assumes the datapoints are i.i.d.")
 
         self.queue = mp.Queue(self.nr_prefetch)
-        self.procs = [MultiProcessPrefetchData._Worker(self.ds, self.queue)
-                      for _ in range(self.nr_proc)]
+        self.procs = [MultiProcessPrefetchData._Worker(self.ds, self.queue, idx)
+                      for idx in range(self.nr_proc)]
         ensure_proc_terminate(self.procs)
         start_proc_mask_signal(self.procs)
 
@@ -249,14 +250,15 @@ class PrefetchDataZMQ(_MultiProcessZMQDataFlow):
     """
 
     class _Worker(mp.Process):
-        def __init__(self, ds, conn_name, hwm):
+        def __init__(self, ds, conn_name, hwm, idx):
             super(PrefetchDataZMQ._Worker, self).__init__()
             self.ds = ds
             self.conn_name = conn_name
             self.hwm = hwm
+            self.idx = idx
 
         def run(self):
-            enable_death_signal()
+            enable_death_signal(_warn=self.idx == 0)
             self.ds.reset_state()
             context = zmq.Context()
             socket = context.socket(zmq.PUSH)
@@ -315,8 +317,8 @@ class PrefetchDataZMQ(_MultiProcessZMQDataFlow):
         pipename = _get_pipe_name('dataflow')
         _bind_guard(self.socket, pipename)
 
-        self._procs = [PrefetchDataZMQ._Worker(self.ds, pipename, self._hwm)
-                       for _ in range(self.nr_proc)]
+        self._procs = [PrefetchDataZMQ._Worker(self.ds, pipename, self._hwm, idx)
+                       for idx in range(self.nr_proc)]
         self._start_processes()
 
 

tensorpack/dataflow/parallel_map.py

@@ -224,7 +224,7 @@ class MultiProcessMapDataZMQ(_ParallelMapData, _MultiProcessZMQDataFlow):
             self.hwm = hwm
 
         def run(self):
-            enable_death_signal()
+            enable_death_signal(_warn=self.identity == b'0')
             ctx = zmq.Context()
             socket = ctx.socket(zmq.REP)
             socket.setsockopt(zmq.IDENTITY, self.identity)

tensorpack/graph_builder/utils.py

@@ -356,7 +356,7 @@ class GradientPacker(object):
         split_size_last = self._total_size - split_size * (self._num_split - 1)
         self._split_sizes = [split_size] * (self._num_split - 1) + [split_size_last]
         logger.info(
-            "Will pack {} gradients of total number={} into {} splits.".format(
+            "Will pack {} gradients of total dimension={} into {} splits.".format(
                 len(self._sizes), self._total_size, self._num_split))
         return True
 

tensorpack/utils/concurrency.py

@@ -173,7 +173,7 @@ def ensure_proc_terminate(proc):
     atexit.register(stop_proc_by_weak_ref, weakref.ref(proc))
 
 
-def enable_death_signal():
+def enable_death_signal(_warn=True):
     """
     Set the "death signal" of the current process, so that
     the current process will be cleaned with guarantee
@@ -184,6 +184,7 @@ def enable_death_signal():
     try:
         import prctl    # pip install python-prctl
     except ImportError:
+        if _warn:
             log_once('Install python-prctl so that processes can be cleaned with guarantee.', 'warn')
         return
     else: