use InputDesc instead of InputVar.

CHANGES.md

@@ -8,6 +8,8 @@ so you won't need to look at here very often.
 Here are a list of things that were changed, starting from an early version.
 TensorFlow itself also changes API and those are not listed here.
 
+* 2017/02/11. `_get_input_vars()` in `ModelDesc` was renamed to `_get_inputs`. `InputVar` was
+	renamed to `InputDesc`.
 * 2017/01/27. `TrainConfig(step_per_epoch)` was renamed to `steps_per_epoch`. See [commit](https://github.com/ppwwyyxx/tensorpack/commit/a9dd0b8ec34209ab86a92875589dbbc4716e73ef).
 * 2017/01/25. Argument order of `models.ConcatWith` is changed to follow the API change in
 	TensorFlow upstream. See [commit](https://github.com/ppwwyyxx/tensorpack/commit/2df3dcf401a99fe61c699ad719e95528872d3abe).

docs/casestudies/colorize.md

@@ -145,8 +145,7 @@ If you are surprised how far we already are, you will enjoy how easy it is to de
 
 ```python
 class Model(ModelDesc):
-
-    def _get_input_vars(self):
+    def _get_inputs(self):
         pass
 
     def _build_graph(self, input_vars):
@@ -154,7 +153,7 @@ class Model(ModelDesc):
 ```
 
 The framework expects:
-- a definition of inputs in `_get_input_vars`
+- a definition of inputs in `_get_inputs`
 - a computation graph containing the actual network layers in `_build_graph`
 - In single-cost optimization problem, a member `self.cost` representing the loss function we would like to minimize.
 
@@ -163,23 +162,23 @@ Our dataflow produces data which looks like `[(32, 256, 256), (32, 256, 256, 3)]
 The first entry is the luminance channel as input and the latter is the original RGB image with all three channels. So we will write
 
 ```python
-def _get_input_vars(self):
-        return [InputVar(tf.float32, (None, 256, 256), 'luminance'),
-                InputVar(tf.int32, (None, 256, 256, 3), 'rgb')]
+def _get_inputs(self):
+        return [InputDesc(tf.float32, (None, 256, 256), 'luminance'),
+                InputDesc(tf.int32, (None, 256, 256, 3), 'rgb')]
 ```
 
 This is pretty straight forward, isn't it? We defined the shapes of the input and give each entry a name.
 You can certainly use 32 instead of `None`, but since the model itself doesn't really need to know
 the batch size, using `None` offers the extra flexibility to run inference with a different batch size in the same graph.
 
-From now, the `input_vars` in `_build_graph(self, input_vars)` will be the tensors of the defined shapes in the method `_get_input_vars`. We can therefore write
+From now, the `input_vars` in `_build_graph(self, input_vars)` will be the tensors of the defined shapes in the method `_get_inputs`. We can therefore write
 
 ```python
 class Model(ModelDesc):
 
-    def _get_input_vars(self):
-        return [InputVar(tf.float32, (None, 256, 256), 'luminance'),
-                InputVar(tf.int32, (None, 256, 256, 3), 'rgb')]
+    def _get_inputs(self):
+        return [InputDesc(tf.float32, (None, 256, 256), 'luminance'),
+                InputDesc(tf.int32, (None, 256, 256, 3), 'rgb')]
 
     def _build_graph(self, input_vars):
         luminance, rgb = input_vars  # (None, 256, 256), (None, 256, 256, 3)
@@ -354,7 +353,7 @@ class OnlineExport(Callback):
         pass
 ```
 
-Can you remember the method `_get_input_vars` in our model? We used the name `luminance` to identify one input.
+Can you remember the method `_get_inputs` in our model? We used the name `luminance` to identify one input.
 If not, this is the best time to go back in this text and read how to specify input variables for the network.
 In the deconvolution step there was also:
 

docs/tutorial/model.md

@@ -7,7 +7,7 @@ you'll need to subclass `ModelDesc` and implement several methods:
 ```python
 class MyModel(ModelDesc):
 	def _get_inputs(self):
-		return [InputVar(...), InputVar(...)]
+		return [InputDesc(...), InputDesc(...)]
 
 	def _build_graph(self, inputs):
 		# build the graph

examples/A3C-Gym/run-atari.py

@@ -41,9 +41,9 @@ def get_player(dumpdir=None):
 class Model(ModelDesc):
     def _get_inputs(self):
         assert NUM_ACTIONS is not None
-        return [InputVar(tf.float32, (None,) + IMAGE_SHAPE3, 'state'),
-                InputVar(tf.int32, (None,), 'action'),
-                InputVar(tf.float32, (None,), 'futurereward')]
+        return [InputDesc(tf.float32, (None,) + IMAGE_SHAPE3, 'state'),
+                InputDesc(tf.int32, (None,), 'action'),
+                InputDesc(tf.float32, (None,), 'futurereward')]
 
     def _get_NN_prediction(self, image):
         image = image / 255.0

examples/A3C-Gym/train-atari.py

@@ -78,9 +78,9 @@ class MySimulatorWorker(SimulatorProcess):
 class Model(ModelDesc):
     def _get_inputs(self):
         assert NUM_ACTIONS is not None
-        return [InputVar(tf.float32, (None,) + IMAGE_SHAPE3, 'state'),
-                InputVar(tf.int64, (None,), 'action'),
-                InputVar(tf.float32, (None,), 'futurereward')]
+        return [InputDesc(tf.float32, (None,) + IMAGE_SHAPE3, 'state'),
+                InputDesc(tf.int64, (None,), 'action'),
+                InputDesc(tf.float32, (None,), 'futurereward')]
 
     def _get_NN_prediction(self, image):
         image = image / 255.0

examples/CTC-TIMIT/train-timit.py

@@ -28,11 +28,11 @@ FEATUREDIM = 39     # MFCC feature dimension
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, None, FEATUREDIM], 'feat'),   # bxmaxseqx39
-                InputVar(tf.int64, None, 'labelidx'),  # label is b x maxlen, sparse
-                InputVar(tf.int32, None, 'labelvalue'),
-                InputVar(tf.int64, None, 'labelshape'),
-                InputVar(tf.int32, [None], 'seqlen'),   # b
+        return [InputDesc(tf.float32, [None, None, FEATUREDIM], 'feat'),   # bxmaxseqx39
+                InputDesc(tf.int64, None, 'labelidx'),  # label is b x maxlen, sparse
+                InputDesc(tf.int32, None, 'labelvalue'),
+                InputDesc(tf.int64, None, 'labelshape'),
+                InputDesc(tf.int32, [None], 'seqlen'),   # b
                 ]
 
     def _build_graph(self, inputs):

examples/Char-RNN/char-rnn.py

@@ -66,8 +66,8 @@ class CharRNNData(RNGDataFlow):
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.int32, (None, param.seq_len), 'input'),
-                InputVar(tf.int32, (None, param.seq_len), 'nextinput')]
+        return [InputDesc(tf.int32, (None, param.seq_len), 'input'),
+                InputDesc(tf.int32, (None, param.seq_len), 'nextinput')]
 
     def _build_graph(self, inputs):
         input, nextinput = inputs

examples/ConvolutionalPoseMachines/load-cpm.py

@@ -45,8 +45,8 @@ def get_gaussian_map():
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, 368, 368, 3), 'input'),
-                InputVar(tf.float32, (None, 368, 368, 15), 'label'),
+        return [InputDesc(tf.float32, (None, 368, 368, 3), 'input'),
+                InputDesc(tf.float32, (None, 368, 368, 15), 'label'),
                 ]
 
     def _build_graph(self, inputs):

examples/DeepQNetwork/DQN.py

@@ -72,11 +72,11 @@ class Model(ModelDesc):
         if NUM_ACTIONS is None:
             p = get_player()
             del p
-        return [InputVar(tf.float32, (None,) + IMAGE_SHAPE3, 'state'),
-                InputVar(tf.int64, (None,), 'action'),
-                InputVar(tf.float32, (None,), 'reward'),
-                InputVar(tf.float32, (None,) + IMAGE_SHAPE3, 'next_state'),
-                InputVar(tf.bool, (None,), 'isOver')]
+        return [InputDesc(tf.float32, (None,) + IMAGE_SHAPE3, 'state'),
+                InputDesc(tf.int64, (None,), 'action'),
+                InputDesc(tf.float32, (None,), 'reward'),
+                InputDesc(tf.float32, (None,) + IMAGE_SHAPE3, 'next_state'),
+                InputDesc(tf.bool, (None,), 'isOver')]
 
     def _get_DQN_prediction(self, image):
         """ image: [0,255]"""

examples/DoReFa-Net/alexnet-dorefa.py

@@ -75,8 +75,8 @@ BATCH_SIZE = None
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, 224, 224, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+        return [InputDesc(tf.float32, [None, 224, 224, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs

examples/DoReFa-Net/resnet-dorefa.py

@@ -34,8 +34,8 @@ BITG = 32
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, 224, 224, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+        return [InputDesc(tf.float32, [None, 224, 224, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs

examples/DoReFa-Net/svhn-digit-dorefa.py

@@ -44,8 +44,8 @@ BITG = 4
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, 40, 40, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+        return [InputDesc(tf.float32, [None, 40, 40, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs

examples/GAN/ConditionalGAN-mnist.py

@@ -27,9 +27,9 @@ BATCH = 128
 
 
 class Model(GANModelDesc):
-    def _get_input_vars(self):
-        return [InputVar(tf.float32, (None, 28, 28), 'input'),
-                InputVar(tf.int32, (None,), 'label')]
+    def _get_inputs(self):
+        return [InputDesc(tf.float32, (None, 28, 28), 'input'),
+                InputDesc(tf.int32, (None,), 'label')]
 
     def generator(self, z, y):
         l = FullyConnected('fc0', tf.concat([z, y], 1), 1024, nl=BNReLU)

examples/GAN/DCGAN-CelebA.py

@@ -36,7 +36,7 @@ Z_DIM = 100
 
 class Model(GANModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, SHAPE, SHAPE, 3), 'input')]
+        return [InputDesc(tf.float32, (None, SHAPE, SHAPE, 3), 'input')]
 
     def generator(self, z):
         """ return a image generated from z"""

examples/GAN/Image2Image.py

@@ -44,8 +44,8 @@ NF = 64  # number of filter
 
 class Model(GANModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, SHAPE, SHAPE, IN_CH), 'input'),
-                InputVar(tf.float32, (None, SHAPE, SHAPE, OUT_CH), 'output')]
+        return [InputDesc(tf.float32, (None, SHAPE, SHAPE, IN_CH), 'input'),
+                InputDesc(tf.float32, (None, SHAPE, SHAPE, OUT_CH), 'output')]
 
     def generator(self, imgs):
         # imgs: input: 256x256xch

examples/GAN/InfoGAN-mnist.py

@@ -41,7 +41,7 @@ class GaussianWithUniformSample(GaussianDistribution):
 
 class Model(GANModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, 28, 28), 'input')]
+        return [InputDesc(tf.float32, (None, 28, 28), 'input')]
 
     def generator(self, z):
         l = FullyConnected('fc0', z, 1024, nl=BNReLU)

examples/HED/hed.py

@@ -18,8 +18,8 @@ from tensorpack.tfutils.summary import *
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, None, None, 3], 'image'),
-                InputVar(tf.int32, [None, None, None], 'edgemap')]
+        return [InputDesc(tf.float32, [None, None, None, 3], 'image'),
+                InputDesc(tf.int32, [None, None, None], 'edgemap')]
 
     def _build_graph(self, inputs):
         image, edgemap = inputs

examples/Inception/inception-bn.py

@@ -30,8 +30,8 @@ Learning rate may need a different schedule for different number of GPUs (becaus
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+        return [InputDesc(tf.float32, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs

examples/Inception/inceptionv3.py

@@ -35,8 +35,8 @@ INPUT_SHAPE = 299
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+        return [InputDesc(tf.float32, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs

examples/PennTreebank/PTB-LSTM.py

@@ -45,8 +45,8 @@ def get_PennTreeBank(data_dir=None):
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.int32, (None, SEQ_LEN), 'input'),
-                InputVar(tf.int32, (None, SEQ_LEN), 'nextinput')]
+        return [InputDesc(tf.int32, (None, SEQ_LEN), 'input'),
+                InputDesc(tf.int32, (None, SEQ_LEN), 'nextinput')]
 
     def _build_graph(self, inputs):
         is_training = get_current_tower_context().is_training

examples/ResNet/cifar10-resnet.py

@@ -38,8 +38,8 @@ class Model(ModelDesc):
         self.n = n
 
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, 32, 32, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+        return [InputDesc(tf.float32, [None, 32, 32, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs

examples/ResNet/imagenet-resnet.py

@@ -29,8 +29,8 @@ DEPTH = None
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.uint8, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+        return [InputDesc(tf.uint8, [None, INPUT_SHAPE, INPUT_SHAPE, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs

examples/ResNet/load-resnet.py

@@ -25,10 +25,9 @@ MODEL_DEPTH = None
 
 
 class Model(ModelDesc):
-
-    def _get_input_vars(self):
-        return [InputVar(tf.float32, [None, 224, 224, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+    def _get_inputs(self):
+        return [InputDesc(tf.float32, [None, 224, 224, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, input_vars):
         image, label = input_vars

examples/Saliency/saliency-maps.py

@@ -17,7 +17,7 @@ IMAGE_SIZE = 224
 
 class Model(tp.ModelDesc):
     def _get_inputs(self):
-        return [tp.InputVar(tf.float32, (IMAGE_SIZE, IMAGE_SIZE, 3), 'image')]
+        return [tp.InputDesc(tf.float32, (IMAGE_SIZE, IMAGE_SIZE, 3), 'image')]
 
     def _build_graph(self, inputs):
         orig_image = inputs[0]

examples/SimilarityLearning/mnist-embeddings.py

@@ -62,9 +62,9 @@ class SiameseModel(EmbeddingModel):
         return ds
 
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, 28, 28), 'input'),
-                InputVar(tf.float32, (None, 28, 28), 'input_y'),
-                InputVar(tf.int32, (None,), 'label')]
+        return [InputDesc(tf.float32, (None, 28, 28), 'input'),
+                InputDesc(tf.float32, (None, 28, 28), 'input_y'),
+                InputDesc(tf.int32, (None,), 'label')]
 
     def _build_graph(self, inputs):
         # get inputs
@@ -105,9 +105,9 @@ class TripletModel(EmbeddingModel):
         return ds
 
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, 28, 28), 'input'),
-                InputVar(tf.float32, (None, 28, 28), 'input_p'),
-                InputVar(tf.float32, (None, 28, 28), 'input_n')]
+        return [InputDesc(tf.float32, (None, 28, 28), 'input'),
+                InputDesc(tf.float32, (None, 28, 28), 'input_p'),
+                InputDesc(tf.float32, (None, 28, 28), 'input_n')]
 
     def loss(self, a, p, n):
         return symbf.triplet_loss(a, p, n, 5., extra=True, scope="loss")

examples/SpatialTransformer/mnist-addition.py

@@ -20,8 +20,8 @@ HALF_DIFF = (IMAGE_SIZE - WARP_TARGET_SIZE) // 2
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE, 2), 'input'),
-                InputVar(tf.int32, (None,), 'label')]
+        return [InputDesc(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE, 2), 'input'),
+                InputDesc(tf.int32, (None,), 'label')]
 
     def _build_graph(self, inputs):
         xys = np.array([(y, x, 1) for y in range(WARP_TARGET_SIZE)

examples/cifar-convnet.py

@@ -29,8 +29,8 @@ class Model(ModelDesc):
         self.cifar_classnum = cifar_classnum
 
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, 30, 30, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')
+        return [InputDesc(tf.float32, [None, 30, 30, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')
                 ]
 
     def _build_graph(self, inputs):

examples/load-alexnet.py

@@ -25,7 +25,7 @@ Usage:
 class Model(ModelDesc):
 
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, 227, 227, 3), 'input')]
+        return [InputDesc(tf.float32, (None, 227, 227, 3), 'input')]
 
     def _build_graph(self, inputs):
         # img: 227x227x3

examples/load-vgg16.py

@@ -25,7 +25,7 @@ Usage:
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, (None, 224, 224, 3), 'input')]
+        return [InputDesc(tf.float32, (None, 224, 224, 3), 'input')]
 
     def _build_graph(self, inputs):
         image = inputs[0]

examples/mnist-convnet.py

@@ -26,8 +26,8 @@ class Model(ModelDesc):
     def _get_inputs(self):
         """Define all the input variables (with type, shape, name) that'll be
         fed into the graph to produce a cost.  """
-        return [InputVar(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
-                InputVar(tf.int32, (None,), 'label')]
+        return [InputDesc(tf.float32, (None, IMAGE_SIZE, IMAGE_SIZE), 'input'),
+                InputDesc(tf.int32, (None,), 'label')]
 
     def _build_graph(self, inputs):
         """This function should build the model which takes the input variables

examples/svhn-digit-convnet.py

@@ -23,8 +23,8 @@ Speed is about 43 it/s on TitanX.
 
 class Model(ModelDesc):
     def _get_inputs(self):
-        return [InputVar(tf.float32, [None, 40, 40, 3], 'input'),
-                InputVar(tf.int32, [None], 'label')]
+        return [InputDesc(tf.float32, [None, 40, 40, 3], 'input'),
+                InputDesc(tf.int32, [None], 'label')]
 
     def _build_graph(self, inputs):
         image, label = inputs

scripts/dump-model-params.py

@@ -25,7 +25,7 @@ with tf.Graph().as_default() as G:
         MODEL = imp.load_source('config_script', args.config).Model
         M = MODEL()
         with TowerContext('', is_training=False):
-            M.build_graph(M.get_input_vars())
+            M.build_graph(M.get_reused_placehdrs())
     else:
         M = ModelFromMetaGraph(args.meta)
 

tensorpack/callbacks/inference_runner.py

@@ -158,7 +158,7 @@ class FeedfreeInferenceRunner(Triggerable):
         Args:
             input (FeedfreeInput): the input to use. Must have ``size()``.
             infs (list): list of :class:`Inferencer` to run.
-            input_names (list): must be a subset of the names of InputVar.
+            input_names (list): must be a subset of the names in InputDesc.
             prefix(str): an prefix used to build the tower. Must be set
                 differently if more than one :class:`FeedfreeInferenceRunner` are used.
         """
@@ -211,7 +211,7 @@ class FeedfreeInferenceRunner(Triggerable):
                         break
                 else:
                     raise ValueError(
-                        "{} doesn't appear in the InputVar of the model!".format(n))
+                        "{} doesn't appear in the InputDesc of the model!".format(n))
             self._input_tensors = model_placehdrs
 
         assert len(self._input_tensors) == len(model_placehdrs), \

tensorpack/models/model_desc.py

@@ -41,6 +41,7 @@ class InputDesc(object):
         return pickle.loads(buf)
 
 
+# TODO print warning?
 InputVar = InputDesc
 
 
@@ -62,6 +63,7 @@ class ModelDesc(object):
         return ret
 
     def get_input_vars(self):
+        # this wasn't a public API anyway
         logger.warn("[Deprecated] get_input_vars() was renamed to get_reused_placehdrs()!")
         return self.get_reused_placehdrs()
 
@@ -95,6 +97,7 @@ class ModelDesc(object):
         """
         :returns: a list of InputDesc
         """
+        # TODO deprecate @ Mar 11
         logger.warn("[Deprecated] _get_input_vars() is renamed to _get_inputs()")
         return self._get_input_vars()