Rename eval_on_ILSVRC12->eval_classification; fix #1194

examples/DoReFa-Net/alexnet-dorefa.py

@@ -18,7 +18,7 @@ from tensorpack.tfutils.varreplace import remap_variables
 from tensorpack.utils.gpu import get_num_gpu
 
 from dorefa import get_dorefa, ternarize
-from imagenet_utils import ImageNetModel, eval_on_ILSVRC12, fbresnet_augmentor, get_imagenet_dataflow
+from imagenet_utils import ImageNetModel, eval_classification, fbresnet_augmentor, get_imagenet_dataflow
 
 """
 This is a tensorpack script for the ImageNet results in paper:
@@ -219,7 +219,7 @@ if __name__ == '__main__':
     if args.eval:
         BATCH_SIZE = 128
         ds = get_data('val')
-        eval_on_ILSVRC12(Model(), get_model_loader(args.load), ds)
+        eval_classification(Model(), get_model_loader(args.load), ds)
         sys.exit()
 
     nr_tower = max(get_num_gpu(), 1)

examples/DoReFa-Net/resnet-dorefa.py

@@ -13,7 +13,7 @@ from tensorpack.dataflow import dataset
 from tensorpack.tfutils.varreplace import remap_variables
 
 from dorefa import get_dorefa
-from imagenet_utils import ImageNetModel, eval_on_ILSVRC12, fbresnet_augmentor
+from imagenet_utils import ImageNetModel, eval_classification, fbresnet_augmentor
 
 """
 This script loads the pre-trained ResNet-18 model with (W,A,G) = (1,4,32)
@@ -163,7 +163,7 @@ if __name__ == '__main__':
         ds = dataset.ILSVRC12(args.data, 'val', shuffle=False)
         ds = AugmentImageComponent(ds, get_inference_augmentor())
         ds = BatchData(ds, 192, remainder=True)
-        eval_on_ILSVRC12(Model(), get_model_loader(args.load), ds)
+        eval_classification(Model(), get_model_loader(args.load), ds)
     elif args.run:
         assert args.load.endswith('.npz')
         run_image(Model(), DictRestore(dict(np.load(args.load))), args.run)

examples/ImageNetModels/imagenet_utils.py

@@ -264,7 +264,11 @@ def fbresnet_mapper(isTrain):
 """
 
 
-def eval_on_ILSVRC12(model, sessinit, dataflow):
+def eval_classification(model, sessinit, dataflow):
+    """
+    Eval a classification model on the dataset. It assumes the model inputs are
+    named "input" and "label", and contains "wrong-top1" and "wrong-top5" in the graph.
+    """
     pred_config = PredictConfig(
         model=model,
         session_init=sessinit,

examples/ImageNetModels/shufflenet.py

@@ -16,7 +16,7 @@ from tensorpack.tfutils.scope_utils import under_name_scope
 from tensorpack.utils import logger
 from tensorpack.utils.gpu import get_num_gpu
 
-from imagenet_utils import ImageNetModel, eval_on_ILSVRC12, get_imagenet_dataflow
+from imagenet_utils import ImageNetModel, eval_classification, get_imagenet_dataflow
 
 
 @layer_register(log_shape=True)
@@ -251,7 +251,7 @@ if __name__ == '__main__':
     if args.eval:
         batch = 128    # something that can run on one gpu
         ds = get_data('val', batch)
-        eval_on_ILSVRC12(model, get_model_loader(args.load), ds)
+        eval_classification(model, get_model_loader(args.load), ds)
     elif args.flops:
         # manually build the graph with batch=1
         with TowerContext('', is_training=False):

examples/ResNet/imagenet-resnet.py

@@ -13,7 +13,7 @@ from tensorpack.tfutils import argscope, get_model_loader
 from tensorpack.train import SyncMultiGPUTrainerReplicated, TrainConfig, launch_train_with_config
 from tensorpack.utils.gpu import get_num_gpu
 
-from imagenet_utils import ImageNetModel, eval_on_ILSVRC12, get_imagenet_dataflow, get_imagenet_tfdata
+from imagenet_utils import ImageNetModel, eval_classification, get_imagenet_dataflow, get_imagenet_tfdata
 from resnet_model import (
     preresnet_basicblock, preresnet_bottleneck, preresnet_group,
     resnet_backbone, resnet_group,
@@ -143,7 +143,7 @@ if __name__ == '__main__':
     if args.eval:
         batch = 128    # something that can run on one gpu
         ds = get_imagenet_dataflow(args.data, 'val', batch)
-        eval_on_ILSVRC12(model, get_model_loader(args.load), ds)
+        eval_classification(model, get_model_loader(args.load), ds)
     else:
         if args.fake:
             logger.set_logger_dir(os.path.join('train_log', 'tmp'), 'd')

examples/ResNet/load-resnet.py

@@ -16,7 +16,7 @@ from tensorpack import *
 from tensorpack.dataflow.dataset import ILSVRCMeta
 from tensorpack.utils import logger
 
-from imagenet_utils import ImageNetModel, eval_on_ILSVRC12, get_imagenet_dataflow
+from imagenet_utils import ImageNetModel, eval_classification, get_imagenet_dataflow
 from resnet_model import resnet_bottleneck, resnet_group
 
 DEPTH = None
@@ -172,6 +172,6 @@ if __name__ == '__main__':
 
     if args.eval:
         ds = get_imagenet_dataflow(args.eval, 'val', 128, get_inference_augmentor())
-        eval_on_ILSVRC12(Model(), DictRestore(param), ds)
+        eval_classification(Model(), DictRestore(param), ds)
     elif args.input:
         run_test(param, args.input)

tensorpack/contrib/keras.py

@@ -98,7 +98,7 @@ class KerasModelCaller(object):
             # NOTE: ctx.is_training won't be useful inside model,
             # because inference will always use the cached Keras model
             model = self.cached_model
-            outputs = model.call(input_tensors)
+            outputs = model.call(*input_tensors)
         else:
             # create new Keras model if not reuse
             model = self.get_model(*input_tensors)

tensorpack/dataflow/image.py

@@ -45,7 +45,7 @@ class ExceptionHandler:
 
 
 class ImageFromFile(RNGDataFlow):
-    """ Produce images read from a list of files. """
+    """ Produce images read from a list of files as (h, w, c) arrays. """
     def __init__(self, files, channel=3, resize=None, shuffle=False):
         """
         Args: