[Incompatible] refactor inferencer. ClassificationError now takes a v…

…ector

README.md

@@ -29,7 +29,7 @@ Describe your training task with three components:
 	+ Use Python to easily handle any of your own data format, yet still keep a good training speed thanks to multiprocess prefetch & TF Queue prefetch.
 	For example, InceptionV3 can run in the same speed as the official code which reads data using TF operators.
 
-3. Callbacks, including everything you want to do apart from the training iterations. Such as:
+3. Callbacks, including everything you want to do apart from the training iterations, such as:
 	+ Change hyperparameters during training
 	+ Print some variables of interest
 	+ Run inference on a test dataset
@@ -49,7 +49,7 @@ Multi-GPU training is off-the-shelf by simply switching the trainer.
 pip install --user -r requirements.txt
 pip install --user -r opt-requirements.txt (some optional dependencies, you can install later if needed)
 ```
-+ Use [tcmalloc](http://goog-perftools.sourceforge.net/doc/tcmalloc.html) whenever possible
++ [tcmalloc](http://goog-perftools.sourceforge.net/doc/tcmalloc.html) usually helps.
 + Enable `import tensorpack`:
 ```
 export PYTHONPATH=$PYTHONPATH:`readlink -f path/to/tensorpack`

examples/DisturbLabel/mnist-disturb.py

@@ -40,7 +40,6 @@ def _build_graph(self, input_vars):
         prob = tf.nn.softmax(logits, name='prob')
 
         wrong = symbolic_functions.prediction_incorrect(logits, label)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong')
         add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)

examples/DoReFa-Net/alexnet-dorefa.py

@@ -142,11 +142,9 @@ def activate(x):
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')
 
-        wrong = prediction_incorrect(logits, label, 1)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong-top1')
+        wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
         add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
-        wrong = prediction_incorrect(logits, label, 5)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong-top5')
+        wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
         add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
 
         # weight decay on all W of fc layers

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

@@ -109,7 +109,6 @@ def activate(x):
 
         # compute the number of failed samples
         wrong = prediction_incorrect(logits, label)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
         add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 

examples/Inception/inception-bn.py

@@ -102,12 +102,10 @@ def inception(name, x, nr1x1, nr3x3r, nr3x3, nr233r, nr233, nrpool, pooltype):
         cost = tf.add_n([loss3, 0.3 * loss2, 0.3 * loss1], name='weighted_cost')
         add_moving_summary([cost, loss1, loss2, loss3])
 
-        wrong = prediction_incorrect(logits, label, 1)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong-top1')
+        wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
         add_moving_summary(tf.reduce_mean(wrong, name='train_error_top1'))
 
-        wrong = prediction_incorrect(logits, label, 5)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong-top5')
+        wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
         add_moving_summary(tf.reduce_mean(wrong, name='train_error_top5'))
 
         # weight decay on all W of fc layers

examples/Inception/inceptionv3.py

@@ -180,12 +180,10 @@ def proj_277(l, ch_r, ch):
         loss2 = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
         loss2 = tf.reduce_mean(loss2, name='loss2')
 
-        wrong = prediction_incorrect(logits, label, 1)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong-top1')
+        wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
         add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
 
-        wrong = prediction_incorrect(logits, label, 5)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong-top5')
+        wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
         add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
 
         # weight decay on all W of fc layers

examples/ResNet/cifar10-resnet.py

@@ -101,7 +101,6 @@ def residual(name, l, increase_dim=False, first=False):
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')
 
         wrong = prediction_incorrect(logits, label)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
         add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 

examples/ResNet/imagenet-resnet.py

@@ -110,12 +110,10 @@ def layer(l, layername, block_func, features, count, stride, first=False):
         loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
         loss = tf.reduce_mean(loss, name='xentropy-loss')
 
-        wrong = prediction_incorrect(logits, label, 1)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong-top1')
+        wrong = prediction_incorrect(logits, label, 1, name='wrong-top1')
         add_moving_summary(tf.reduce_mean(wrong, name='train-error-top1'))
 
-        wrong = prediction_incorrect(logits, label, 5)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong-top5')
+        wrong = prediction_incorrect(logits, label, 5, name='wrong-top5')
         add_moving_summary(tf.reduce_mean(wrong, name='train-error-top5'))
 
         # weight decay on all W of fc layers

examples/SpatialTransformer/mnist-addition.py

@@ -76,7 +76,6 @@ def get_stn(image):
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')
 
         wrong = symbolic_functions.prediction_incorrect(logits, label)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong')
         summary.add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 
         wd_cost = tf.mul(1e-5, regularize_cost('fc.*/W', tf.nn.l2_loss),

examples/cifar-convnet.py

@@ -59,9 +59,7 @@ def _build_graph(self, input_vars):
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')
 
-        # compute the number of failed samples, for ClassificationError to use at test time
         wrong = symbf.prediction_incorrect(logits, label)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
         add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 

examples/mnist-convnet.py

@@ -62,9 +62,8 @@ def _build_graph(self, input_vars):
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, label) # a vector of length B with loss of each sample
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')  # the average cross-entropy loss
 
-        # compute the number of failed samples, for thee callback ClassificationError to use at test time
-        wrong = symbolic_functions.prediction_incorrect(logits, label)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong')
+        # compute the "incorrect vector", for the callback ClassificationError to use at validation time
+        wrong = symbolic_functions.prediction_incorrect(logits, label, name='incorrect')
 
         # This will monitor training error (in a moving_average fashion):
         # 1. write the value to tensosrboard
@@ -117,7 +116,7 @@ def get_config():
             InferenceRunner(    # run inference(for validation) after every epoch
                 dataset_test,   # the DataFlow instance used for validation
                 # Calculate both the cost and the error for this DataFlow
-                [ScalarStats('cost'), ClassificationError() ]),
+                [ScalarStats('cost'), ClassificationError('incorrect')]),
         ]),
         model=Model(),
         step_per_epoch=step_per_epoch,

examples/svhn-digit-convnet.py

@@ -46,7 +46,6 @@ def _build_graph(self, input_vars):
 
         # compute the number of failed samples, for ClassificationError to use at test time
         wrong = prediction_incorrect(logits, label)
-        nr_wrong = tf.reduce_sum(wrong, name='wrong')
         # monitor training error
         add_moving_summary(tf.reduce_mean(wrong, name='train_error'))
 

tensorpack/callbacks/inference.py

@@ -11,10 +11,11 @@
 from six.moves import zip, map
 
 from ..dataflow import DataFlow
-from ..utils import get_tqdm_kwargs, logger
+from ..utils import get_tqdm_kwargs, logger, execute_only_once
 from ..utils.stat import RatioCounter, BinaryStatistics
 from ..tfutils import get_op_tensor_name, get_op_var_name
 from .base import Callback
+from .dispatcher import OutputTensorDispatcer
 
 __all__ = ['InferenceRunner', 'ClassificationError',
         'ScalarStats', 'Inferencer', 'BinaryClassificationStats']
@@ -31,14 +32,14 @@ def before_inference(self):
     def _before_inference(self):
         pass
 
-    def datapoint(self, dp, output):
+    def datapoint(self, _, output):
         """
         Called after complete running every data point
         """
-        self._datapoint(dp, output)
+        self._datapoint(_, output)
 
     @abstractmethod
-    def _datapoint(self, dp, output):
+    def _datapoint(self, _, output):
         pass
 
     def after_inference(self):
@@ -97,21 +98,24 @@ def _find_input_tensors(self):
             self.input_tensors = [x.name for x in input_vars]
 
     def _find_output_tensors(self):
+        dispatcer = OutputTensorDispatcer()
+        for inf in self.infs:
+            dispatcer.add_entry(inf.get_output_tensors())
+        all_names = dispatcer.get_all_names()
+
         IOTensor = InferenceRunner.IOTensor
-        self.output_tensors = []
-        def find_oid(t):
-            tensorname = get_op_tensor_name(t)[1]
-            if tensorname in self.input_tensors:
-                # this inferencer needs the input dp
-                return IOTensor(self.input_tensors.index(tensorname), False)
-            if t in self.output_tensors:
-                return IOTensor(self.output_tensors.index(t), True)
-            else:
-                self.output_tensors.append(t)
-                return IOTensor(len(self.output_tensors) - 1, True)
-        self.inf_to_tensors = [
-                [find_oid(t) for t in inf.get_output_tensors()]
-                for inf in self.infs]
+        self.output_tensors = list(filter(
+            lambda x: x not in self.input_tensors, all_names))
+        def find_oid(idxs):
+            ret = []
+            for idx in idxs:
+                name = all_names[idx]
+                if name in self.input_tensors:
+                    ret.append(IOTensor(self.input_tensors.index(name), False))
+                else:
+                    ret.append(IOTensor(self.output_tensors.index(name), True))
+            return ret
+        self.inf_to_tensors = [find_oid(t) for t in dispatcer.get_idx_for_each_entry()]
         # list of list of (var_name: IOTensor)
 
     def _trigger_epoch(self):
@@ -162,7 +166,7 @@ def _get_output_tensors(self):
     def _before_inference(self):
         self.stats = []
 
-    def _datapoint(self, dp, output):
+    def _datapoint(self, _, output):
         self.stats.append(output)
 
     def _after_inference(self):
@@ -180,15 +184,16 @@ class ClassificationError(Inferencer):
     """
     Compute classification error in batch mode, from a `wrong` variable
 
-    The `wrong` variable is supposed to be an integer equal to the number of failed samples in this batch.
-    You can use `tf.nn.in_top_k` to record top-k error as well.
+    The `wrong` tensor is supposed to be an 0/1 integer vector containing
+    whether each sample in the batch is incorrectly classified.
+    You can use `tf.nn.in_top_k` to produce this vector record top-k error as well.
 
     This callback produce the "true" error,
     taking account of the fact that batches might not have the same size in
     testing (because the size of test set might not be a multiple of batch size).
     Therefore the result is different from averaging the error rate of each batch.
     """
-    def __init__(self, wrong_var_name='wrong:0', summary_name='val_error'):
+    def __init__(self, wrong_var_name='incorrect_vector', summary_name='val_error'):
         """
         :param wrong_var_name: name of the `wrong` variable
         :param summary_name: the name for logging
@@ -202,9 +207,18 @@ def _get_output_tensors(self):
     def _before_inference(self):
         self.err_stat = RatioCounter()
 
-    def _datapoint(self, dp, outputs):
-        batch_size = dp[0].shape[0]   # assume batched input
-        wrong = int(outputs[0])
+    def _datapoint(self, _, outputs):
+        vec = outputs[0]
+        if vec.ndim == 0:
+            if execute_only_once():
+                logger.warn("[DEPRECATED] use a 'wrong vector' for ClassificationError instead of nr_wrong")
+            batch_size = _[0].shape[0]   # assume batched input
+            wrong = int(vec)
+        else:
+            # TODO put shape assertion into inferencerrunner
+            assert vec.ndim == 1, "{} is not a vector!".format(self.wrong_var_name)
+            batch_size = len(vec)
+            wrong = np.sum(vec)
         self.err_stat.feed(wrong, batch_size)
 
     def _after_inference(self):
@@ -230,7 +244,7 @@ def _get_output_tensors(self):
     def _before_inference(self):
         self.stat = BinaryStatistics()
 
-    def _datapoint(self, dp, outputs):
+    def _datapoint(self, _, outputs):
         pred, label = outputs
         self.stat.feed(pred, label)
 

tensorpack/tfutils/symbolic_functions.py

@@ -6,13 +6,14 @@
 import numpy as np
 from ..utils import logger
 
-def prediction_incorrect(logits, label, topk=1):
+def prediction_incorrect(logits, label, topk=1, name='incorrect_vector'):
     """
     :param logits: NxC
     :param label: N
     :returns: a float32 vector of length N with 0/1 values, 1 meaning incorrect prediction
     """
-    return tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, topk)), tf.float32)
+    return tf.cast(tf.logical_not(tf.nn.in_top_k(logits, label, topk)),
+            tf.float32, name=name)
 
 def flatten(x):
     """

tensorpack/train/multigpu.py

@@ -143,7 +143,7 @@ def _trigger_epoch(self):
             async_step_total_cnt = int(re.findall(
                 '[0-9]+', self.async_step_counter.__str__())[0])
             self.write_scalar_summary(
-                    'async-global-step', async_step_total_cnt)
+                    'async_global_step', async_step_total_cnt)
         except:
-            logger.exception("Cannot log async-global-step")
+            logger.exception("Cannot log async_global_step")
         super(AsyncMultiGPUTrainer, self)._trigger_epoch()

tensorpack/train/trainer.py

@@ -115,7 +115,6 @@ def __init__(self, trainer):
 
     def run(self):
         self.dataflow.reset_state()
-
         with self.sess.as_default():
             try:
                 while True:

tensorpack/utils/utils.py

@@ -94,6 +94,12 @@ def get_rng(obj=None):
 
 _EXECUTE_HISTORY = set()
 def execute_only_once():
+    """
+    when called with:
+        if execute_only_once():
+            # do something
+    The body is guranteed to be executed only the first time.
+    """
     f = inspect.currentframe().f_back
     ident =  (f.f_code.co_filename, f.f_lineno)
     if ident in _EXECUTE_HISTORY: