update docs

docs/tutorial/inference.md

@@ -116,7 +116,7 @@ training:
 
 1. The model (the graph): you've already written it yourself with TF symbolic functions.
    Nothing about it is related to the tensorpack interface.
-   If you use tensorpack layers, they are mainly just wrappers around `tf.layers`.
+   If you use tensorpack layers, they are not so different from `tf.layers`.
 
 2. The trained parameters: tensorpack saves them in standard TF checkpoint format.
    Nothing about the format is related to tensorpack.
@@ -139,6 +139,8 @@ with TowerContext('', is_training=False):
 ```eval_rst
 .. note:: **Do not use metagraph for inference!**
 
+	Tensorpack saves a metagraph during training. Users should not try to load it for inference.
+
 	Metagraph is the wrong abstraction for a "model".
 	It stores the entire graph which contains not only the mathematical model, but also all the
 	training settings (queues, iterators, summaries, evaluations, multi-gpu replications).

examples/A3C-Gym/README.md

@@ -28,7 +28,7 @@ Some practicical notes:
 
 ### To test a model:
 
-Download models from [model zoo](http://models.tensorpack.com/OpenAIGym/).
+Download models from [model zoo](http://models.tensorpack.com/#OpenAIGym).
 
 Watch the agent play:
 `./train-atari.py --task play --env Breakout-v0 --load Breakout-v0.npz`

examples/CaffeModels/README.md

 
 Example code to convert, load and run inference of some Caffe models.
 Require caffe python bindings to be installed.
-Converted models can also be found at [tensorpack model zoo](http://models.tensorpack.com).
-
+Converted models can also be found at [tensorpack model zoo](http://models.tensorpack.com/#Caffe-Converted).
 ## AlexNet:
 
 Download: https://github.com/BVLC/caffe/tree/master/models/bvlc_alexnet

examples/DoReFa-Net/README.md

@@ -46,7 +46,7 @@ In this implementation, quantized operations are all performed through `tf.float
 + Look at the docstring in `*-dorefa.py` to see detailed usage and performance.
 
 Pretrained model for (1,4,32)-ResNet18 and several AlexNet are available at
-[tensorpack model zoo](http://models.tensorpack.com/DoReFa-Net/).
+[tensorpack model zoo](http://models.tensorpack.com/#DoReFa-Net).
 They're provided in the format of numpy dictionary.
 The __binary-weight 4-bit-activation ResNet-18__ model has 59.2% top-1 validation accuracy.
 

examples/FasterRCNN/config.py

@@ -140,7 +140,7 @@ _C.TRAIN.STARTING_EPOCH = 1  # the first epoch to start with, useful to continue
 # Therefore, there is *no need* to modify the config if you only change the number of GPUs.
 
 _C.TRAIN.LR_SCHEDULE = "1x"      # "1x" schedule in detectron
-_C.TRAIN.EVAL_PERIOD = 25  # period (epochs) to run evaluation
+_C.TRAIN.EVAL_PERIOD = 50  # period (epochs) to run evaluation
 _C.TRAIN.CHECKPOINT_PERIOD = 20  # period (epochs) to save model
 
 # preprocessing --------------------

examples/GAN/BEGAN.py

@@ -17,7 +17,7 @@ from GAN import GANModelDesc, GANTrainer
 Boundary Equilibrium GAN.
 See the docstring in DCGAN.py for usage.
 
-A pretrained model on CelebA is at http://models.tensorpack.com/GAN/
+A pretrained model on CelebA is at http://models.tensorpack.com/#GAN
 """
 
 

examples/GAN/DCGAN.py

@@ -30,7 +30,7 @@ from GAN import GANModelDesc, GANTrainer, RandomZData
 You can also train on other images (just use any directory of jpg files in
 `--data`). But you may need to change the preprocessing.
 
-A pretrained model on CelebA is at http://models.tensorpack.com/GAN/
+A pretrained model on CelebA is at http://models.tensorpack.com/#GAN
 """
 
 

examples/GAN/InfoGAN-mnist.py

@@ -24,7 +24,7 @@ To train:
 To visualize:
     ./InfoGAN-mnist.py --sample --load path/to/model
 
-A pretrained model is at http://models.tensorpack.com/GAN/
+A pretrained model is at http://models.tensorpack.com/#GAN
 """
 
 BATCH = 128

examples/HED/README.md

@@ -33,4 +33,4 @@ To inference (produce a heatmap at each level at out*.png):
 ```bash
 ./hed.py --load pretrained.model --run a.jpg
 ```
-Models I trained can be downloaded [here](http://models.tensorpack.com/HED/).
+Models I trained can be downloaded [here](http://models.tensorpack.com/#HED).

examples/ImageNetModels/README.md

@@ -4,7 +4,7 @@ ImageNet training code of ResNet, ShuffleNet, DoReFa-Net, AlexNet, Inception, VG
 To train any of the models, just do `./{model}.py --data /path/to/ilsvrc`.
 More options are available in `./{model}.py --help`.
 Expected format of data directory is described in [docs](http://tensorpack.readthedocs.io/modules/dataflow.dataset.html#tensorpack.dataflow.dataset.ILSVRC12).
-Some pretrained models can be downloaded at [tensorpack model zoo](http://models.tensorpack.com/).
+Some pretrained models can be downloaded at [tensorpack model zoo](http://models.tensorpack.com/#ImageNetModels).
 
 ### ShuffleNet
 

examples/Saliency/README.md

@@ -39,7 +39,7 @@ Usage:
 ./CAM-resnet.py --data /path/to/imagenet [--load ImageNet-ResNet18-Preact.npz] [--gpu 0,1,2,3]
 ```
 Pretrained and fine-tuned ResNet can be downloaded
-in the [model zoo](http://models.tensorpack.com/).
+in the [model zoo](http://models.tensorpack.com/#Visualization).
 
 2. Generate CAM on ImageNet validation set:
 ```bash

examples/SpatialTransformer/README.md

@@ -20,7 +20,7 @@ To train (takes about 300 epochs to reach 8.8% error):
 ./mnist-addition.py
 ```
 
-To draw the above visualization with [pretrained model](http://models.tensorpack.com/SpatialTransformer/):
+To draw the above visualization with [pretrained model](http://models.tensorpack.com/#SpatialTransformer):
 ```bash
 ./mnist-addition.py --load mnist-addition.npz --view
 ```

examples/SuperResolution/README.md

@@ -35,7 +35,7 @@ python enet-pat.py --vgg19 /path/to/vgg19.npz --data train2017.lmdb
 
 Training is highly unstable and does not often give good results.
 The pretrained model may also fail on different types of images.
-You can download and play with the pretrained model [here](http://models.tensorpack.com/SuperResolution/).
+You can download and play with the pretrained model [here](http://models.tensorpack.com/#SuperResolution).
 
 3. Inference on an image and output in current directory:
 

tensorpack/graph_builder/training.py

@@ -304,7 +304,7 @@ class SyncMultiGPUReplicatedBuilder(DataParallelBuilder):
             with tf.name_scope('sync_variables'):
                 post_init_op = SyncMultiGPUReplicatedBuilder.get_post_init_ops()
         else:
-            post_init_op = tf.no_op(name='empty_sync_variables')
+            post_init_op = None
         return train_op, post_init_op
 
 # Adopt from https://github.com/tensorflow/benchmarks/blob/master/scripts/tf_cnn_benchmarks/variable_mgr.py

tensorpack/train/trainers.py

@@ -190,6 +190,7 @@ class SyncMultiGPUTrainerReplicated(SingleCostTrainer):
         grad_list = self._builder.call_for_each_tower(tower_fn)
         self.train_op, post_init_op = self._builder.build(grad_list, get_opt_fn)
 
+        if post_init_op is not None:
             cb = RunOp(
                 post_init_op,
                 run_before=True,
@@ -197,6 +198,8 @@ class SyncMultiGPUTrainerReplicated(SingleCostTrainer):
                 verbose=True)
             cb.name_scope = "SyncVariables"
             return [cb]
+        else:
+            return []
 
 
 class DistributedTrainerBase(SingleCostTrainer):