Do not require ALE if using DQN with gym only (fix #1091)

docs/conf.py

@@ -401,6 +401,7 @@ _DEPRECATED_NAMES = set([
     'average_grads',
     'aggregate_grads',
     'allreduce_grads',
+    'get_checkpoint_path'
 ])
 
 def autodoc_skip_member(app, what, name, obj, skip, options):

docs/tutorial/save-load.md

@@ -10,8 +10,8 @@ Both are necessary.
 
 `tf.train.NewCheckpointReader` is the offical tool to parse TensorFlow checkpoint.
 Read [TF docs](https://www.tensorflow.org/api_docs/python/tf/train/NewCheckpointReader) for details.
-Tensorpack also provides some small tools to work with checkpoints, see 
-[documentation](../modules/tfutils.html#tensorpack.tfutils.varmanip.load_chkpt_vars)
+Tensorpack also provides a small tool to load checkpoints, see 
+[load_chkpt_vars](../modules/tfutils.html#tensorpack.tfutils.varmanip.load_chkpt_vars)
 for details.
 
 [scripts/ls-checkpoint.py](../scripts/ls-checkpoint.py)
@@ -19,19 +19,28 @@ demos how to print all variables and their shapes in a checkpoint.
 
 [scripts/dump-model-params.py](../scripts/dump-model-params.py) can be used to remove unnecessary variables in a checkpoint.
 It takes a metagraph file (which is also saved by `ModelSaver`) and only saves variables that the model needs at inference time.
-It can dump the model to a `var-name: value` dict saved in npz format.
+It dumps the model to a `var-name: value` dict saved in npz format.
 
 ## Load a Model to a Session
 
-Model loading (in either training or inference) is through the `session_init` interface.
+Model loading (in both training and inference) is through the `session_init` interface.
 Currently there are two ways a session can be restored:
 [session_init=SaverRestore(...)](../modules/tfutils.html#tensorpack.tfutils.sessinit.SaverRestore)
 which restores a TF checkpoint,
 or [session_init=DictRestore(...)](../modules/tfutils.html#tensorpack.tfutils.sessinit.DictRestore) which restores a dict.
-[get_model_loader](../modules/tfutils.html#tensorpack.tfutils.sessinit.get_model_loader)
-is a small helper to decide which one to use from a file name.
 To load multiple models, use [ChainInit](../modules/tfutils.html#tensorpack.tfutils.sessinit.ChainInit).
 
+Many models in tensorpack model zoo are provided in the form of numpy dictionary (`.npz`),
+because it is easier to load and manipulate without requiring TensorFlow.
+To load such files to a session, use `DictRestore(dict(np.load(filename)))`.
+You can also use
+[get_model_loader](../modules/tfutils.html#tensorpack.tfutils.sessinit.get_model_loader),
+a small helper to create a `SaverRestore` or `DictRestore` based on the file name.
+
+`DictRestore` is the most general loader because you can make arbitrary changes
+you need (e.g., remove variables, rename variables) to the dict.
+To load a TF checkpoint into a dict in order to make changes, use
+[load_chkpt_vars](../modules/tfutils.html#tensorpack.tfutils.varmanip.load_chkpt_vars).
 
 Variable restoring is completely based on __name match__ between
 variables in the current graph and variables in the `session_init` initializer.
@@ -40,4 +49,5 @@ Variables that appear in only one side will be printed as warning.
 ## Transfer Learning
 Therefore, transfer learning is trivial.
 If you want to load a pre-trained model, just use the same variable names.
-If you want to re-train some layer, just rename it.
+If you want to re-train some layer, just rename either the variables in the
+graph or the variables in your loader.

examples/DeepQNetwork/DQN.py

@@ -12,7 +12,6 @@ import tensorflow as tf
 
 from tensorpack import *
 
-from atari import AtariPlayer
 from atari_wrapper import FireResetEnv, FrameStack, LimitLength, MapState
 from common import Evaluator, eval_model_multithread, play_n_episodes
 from DQNModel import Model as DQNModel
@@ -52,6 +51,7 @@ def get_player(viz=False, train=False):
     if USE_GYM:
         env = gym.make(ENV_NAME)
     else:
+        from atari import AtariPlayer
         env = AtariPlayer(ENV_NAME, frame_skip=ACTION_REPEAT, viz=viz,
                           live_lost_as_eoe=train, max_num_frames=60000)
     env = FireResetEnv(env)

examples/DeepQNetwork/expreplay.py

@@ -31,7 +31,11 @@ class ReplayMemory(object):
         self._shape3d = (state_shape[0], state_shape[1], self._channel * (history_len + 1))
         self.history_len = int(history_len)
 
-        self.state = np.zeros((self.max_size,) + state_shape, dtype='uint8')
+        state_shape = (self.max_size,) + state_shape
+        logger.info("Creating experience replay buffer of {:.1f} GB ... "
+                    "use a smaller buffer if you don't have enough CPU memory.".format(
+                        np.prod(state_shape) / 1024.0**3))
+        self.state = np.zeros(state_shape, dtype='uint8')
         self.action = np.zeros((self.max_size,), dtype='int32')
         self.reward = np.zeros((self.max_size,), dtype='float32')
         self.isOver = np.zeros((self.max_size,), dtype='bool')

examples/DoReFa-Net/README.md

@@ -45,7 +45,7 @@ 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 (1,2,6)-AlexNet are available at
+Pretrained model for (1,4,32)-ResNet18 and several AlexNet are available at
 [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.