fix import

examples/ResNet/imagenet-resnet-se.py

@@ -119,7 +119,7 @@ if __name__ == '__main__':
     if args.eval:
         BATCH_SIZE = 128  # something that can run on one gpu
         ds = get_data('val')
-        eval_on_ILSVRC12(Model(), args.load, ds)
+        eval_on_ILSVRC12(Model(), get_model_loader(args.load), ds)
         sys.exit()
 
     logger.set_logger_dir(

examples/ResNet/imagenet-resnet.py

@@ -9,14 +9,14 @@ import os
 
 import tensorflow as tf
 
-from tensorpack import InputDesc, ModelDesc, logger
+from tensorpack import InputDesc, ModelDesc, logger, QueueInput
 from tensorpack.models import *
 from tensorpack.callbacks import *
 from tensorpack.train import TrainConfig, SyncMultiGPUTrainerParameterServer
 from tensorpack.dataflow import imgaug, FakeData
 import tensorpack.tfutils.symbolic_functions as symbf
 from tensorpack.tfutils.summary import add_moving_summary
-from tensorpack.tfutils import argscope, SaverRestore
+from tensorpack.tfutils import argscope, get_model_loader
 from tensorpack.utils.gpu import get_nr_gpu
 
 from imagenet_resnet_utils import (
@@ -137,12 +137,12 @@ if __name__ == '__main__':
     if args.eval:
         BATCH_SIZE = 128    # something that can run on one gpu
         ds = get_data('val')
-        eval_on_ILSVRC12(Model(), args.load, ds)
+        eval_on_ILSVRC12(Model(), get_model_loader(args.load), ds)
         sys.exit()
 
     logger.set_logger_dir(
         os.path.join('train_log', 'imagenet-resnet-d' + str(DEPTH)))
     config = get_config(fake=args.fake, data_format=args.data_format)
     if args.load:
-        config.session_init = SaverRestore(args.load)
+        config.session_init = get_model_loader(args.load)
     SyncMultiGPUTrainerParameterServer(config).train()

examples/ResNet/imagenet_resnet_utils.py

@@ -179,10 +179,10 @@ def resnet_backbone(image, num_blocks, block_func):
     return logits
 
 
-def eval_on_ILSVRC12(model, model_file, dataflow):
+def eval_on_ILSVRC12(model, sessinit, dataflow):
     pred_config = PredictConfig(
         model=model,
-        session_init=tp.get_model_loader(model_file),
+        session_init=sessinit,
         input_names=['input', 'label'],
         output_names=['wrong-top1', 'wrong-top5']
     )

examples/ResNet/load-resnet.py

@@ -21,6 +21,8 @@ from tensorpack.tfutils.symbolic_functions import *
 from tensorpack.tfutils.summary import *
 from tensorpack.dataflow.dataset import ILSVRCMeta, ILSVRC12
 
+from imagenet_resnet_utils import eval_on_ILSVRC12
+
 MODEL_DEPTH = None
 
 
@@ -133,26 +135,6 @@ def run_test(params, input):
     print([meta[k] for k in ret])
 
 
-def eval_on_ILSVRC12(params, data_dir):
-    ds = ILSVRC12(data_dir, 'val', shuffle=False, dir_structure='train')
-    ds = AugmentImageComponent(ds, get_inference_augmentor())
-    ds = BatchData(ds, 128, remainder=True)
-    pred_config = PredictConfig(
-        model=Model(),
-        session_init=DictRestore(params),
-        input_names=['input', 'label'],
-        output_names=['wrong-top1', 'wrong-top5']
-    )
-    pred = SimpleDatasetPredictor(pred_config, ds)
-    acc1, acc5 = RatioCounter(), RatioCounter()
-    for o in pred.get_result():
-        batch_size = o[0].shape[0]
-        acc1.feed(o[0].sum(), batch_size)
-        acc5.feed(o[1].sum(), batch_size)
-    print("Top1 Error: {}".format(acc1.ratio))
-    print("Top5 Error: {}".format(acc5.ratio))
-
-
 def name_conversion(caffe_layer_name):
     """ Convert a caffe parameter name to a tensorflow parameter name as
         defined in the above model """
@@ -221,6 +203,10 @@ if __name__ == '__main__':
         resnet_param[newname] = v
 
     if args.eval:
-        eval_on_ILSVRC12(resnet_param, args.eval)
+        ds = ILSVRC12(args.eval, 'val', shuffle=False, dir_structure='train')
+        ds = AugmentImageComponent(ds, get_inference_augmentor())
+        ds = BatchData(ds, 128, remainder=True)
+        ds = PrefetchDataZMQ(ds, 1)
+        eval_on_ILSVRC12(Model(), DictRestore(resnet_param), ds)
     else:
         run_test(resnet_param, args.input)

tensorpack/dataflow/dataset/ilsvrc.py

@@ -127,6 +127,9 @@ class ILSVRC12Files(RNGDataFlow):
         meta = ILSVRCMeta(meta_dir)
         self.imglist = meta.get_image_list(name, dir_structure)
 
+        for fname, _ in self.imglist[:10]:
+            assert os.path.isfile(fname), fname
+
     def size(self):
         return len(self.imglist)