minor changes

examples/ResNet/imagenet-resnet.py

@@ -94,7 +94,7 @@ def get_config(model, fake=False):
         model=model,
         dataflow=dataset_train,
         callbacks=callbacks,
-        steps_per_epoch=5000,
+        steps_per_epoch=100 if args.fake else 5000,  # 5000 ~= 1.28M / TOTAL_BATCH_SIZE
         max_epoch=110,
         nr_tower=nr_tower
     )
@@ -124,8 +124,11 @@ if __name__ == '__main__':
         ds = get_data('val', batch)
         eval_on_ILSVRC12(model, get_model_loader(args.load), ds)
     else:
-        logger.set_logger_dir(
-            os.path.join('train_log', 'imagenet-resnet-d' + str(args.depth)))
+        if args.fake:
+            logger.set_logger_dir(os.path.join('train_log', 'tmp'), 'd')
+        else:
+            logger.set_logger_dir(
+                os.path.join('train_log', 'imagenet-resnet-d' + str(args.depth)))
 
         config = get_config(model, fake=args.fake)
         if args.load:

examples/ResNet/imagenet_utils.py

@@ -92,7 +92,7 @@ def get_imagenet_dataflow(
     assert datadir is not None
     assert isinstance(augmentors, list)
     isTrain = name == 'train'
-    cpu = min(30, multiprocessing.cpu_count())
+    cpu = min(40, multiprocessing.cpu_count())
     if isTrain:
         ds = dataset.ILSVRC12(datadir, name, shuffle=True)
         ds = AugmentImageComponent(ds, augmentors, copy=False)
@@ -213,3 +213,21 @@ class ImageNetModel(ModelDesc):
         wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
         add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
         return loss
+
+
+if __name__ == '__main__':
+    import argparse
+    from tensorpack.dataflow import TestDataSpeed
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--data', required=True)
+    parser.add_argument('--batch', type=int, default=32)
+    args = parser.parse_args()
+
+    augs = fbresnet_augmentor(False)
+    augs = [imgaug.ResizeShortestEdge(256),
+            imgaug.CenterCrop(224)
+            ]
+    df = get_imagenet_dataflow(
+        args.data, 'train', args.batch, augs)
+
+    TestDataSpeed(df).start()

tensorpack/contrib/keras.py

@@ -17,7 +17,6 @@ from ..callbacks import (
 from ..tfutils.common import get_op_tensor_name
 from ..tfutils.tower import get_current_tower_context
 from ..tfutils.scope_utils import cached_name_scope
-# from ..tfutils.collection import freeze_collection    # TODO freeze UPDATE_OPS in replicated
 from ..tfutils.summary import add_moving_summary
 from ..utils.gpu import get_nr_gpu
 
@@ -108,8 +107,6 @@ def setup_keras_trainer(
     nr_inputs = len(inputs_desc)
 
     def get_cost(*inputs):
-        assert len(inputs) == len(inputs_desc) + len(targets_desc), \
-            "Input source size {} != {} + {}".format(len(inputs), len(inputs_desc), len(targets_desc))
         ctx = get_current_tower_context()
         input_tensors = list(inputs[:nr_inputs])
         target_tensors = list(inputs[nr_inputs:])

tensorpack/dataflow/common.py

@@ -263,7 +263,7 @@ class MapData(ProxyDataFlow):
 
     def get_data(self):
         for dp in self.ds.get_data():
-            ret = self.func(dp)
+            ret = self.func(copy(dp))  # shallow copy the list
             if ret is not None:
                 yield ret
 
@@ -292,7 +292,7 @@ class MapDataComponent(MapData):
             r = func(dp[index])
             if r is None:
                 return None
-            dp = copy(dp)   # avoid modifying the list
+            dp = copy(dp)   # shallow copy to avoid modifying the list
             dp[index] = r
             return dp
         super(MapDataComponent, self).__init__(ds, f)

tensorpack/tfutils/scope_utils.py

@@ -96,7 +96,8 @@ def cached_name_scope(name, top_level=True):
     """
     if not top_level:
         current_ns = tf.get_default_graph().get_name_scope()
-        name = current_ns + '/' + name
+        if current_ns:
+            name = current_ns + '/' + name
     ns = _get_cached_ns(name)
     with tf.name_scope(ns):
         yield ns