udpate docs about load-resnet

examples/ResNet/README.md

 
 ImageNet training code coming soon.
 
+## load-resnet.py
+
+A script to convert and run ResNet{50,101,152} caffe models trained on ImageNet [released by Kaiming](https://github.com/KaimingHe/deep-residual-networks).
+
+Example usage:
+```bash
+# convert the model
+python -m tensorpack.utils.loadcaffe PATH/TO/{ResNet-101-deploy.prototxt,ResNet-101-model.caffemodel} ResNet101.npy
+# run on an image
+./load-resnet.py --load ResNet-101.npy --input cat.jpg --depth 101
+# validate on ILSVRC12
+./load-resnet.py --load ResNet-101.npy --eval PATH/TO/ILSVRC12 --depth 101
+```
+
+The converted models are verified on ILSVRC12 validation set.
+The per-pixel mean used here is slightly different from the original.
+
+| Model              | Top 5 Error | Top 1 Error |
+|:-------------------|-------------|------------:|
+| ResNet 50          |      7.89%  |      25.03% |
+| ResNet 101         |      7.16%  |      23.74% |
+| ResNet 152         |      6.81%  |      23.28% |
+
 ## cifar10-resnet.py
 
 Reproduce the results in paper "Deep Residual Learning for Image Recognition", [http://arxiv.org/abs/1512.03385](http://arxiv.org/abs/1512.03385)
@@ -15,14 +38,3 @@ Download model:
 [Cifar10 ResNet-110 (n=18)](https://drive.google.com/open?id=0B9IPQTvr2BBkTXBlZmh1cmlnQ0k)
 
 Also see an implementation of [DenseNet](https://github.com/YixuanLi/densenet-tensorflow) from [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993).
-
-## load-resnet.py
-
-A script to convert and run ResNet{50,101,152} ImageNet caffe models [released by Kaiming](https://github.com/KaimingHe/deep-residual-networks).
-
-Example usage:
-```bash
-python -m tensorpack.utils.loadcaffe PATH/TO/{ResNet-101-deploy.prototxt,ResNet-101-model.caffemodel} ResNet101.npy
-./load-resnet.py --load ResNet-101.npy --input cat.png --depth 101
-```
-

tensorpack/tfutils/sessinit.py

@@ -58,6 +58,8 @@ class SaverRestore(SessionInit):
         :param prefix: add a `prefix/` for every variable in this checkpoint
         """
         assert os.path.isfile(model_path)
+        if os.path.basename(model_path) == model_path:
+            model_path = os.path.join('.', model_path)  # avoid #4921
         if os.path.basename(model_path) == 'checkpoint':
             model_path = tf.train.get_checkpoint_state(
                 os.path.dirname(model_path)).model_checkpoint_path