fix bug in inferencer, and better naming

scripts/plot-point.py

@@ -9,7 +9,7 @@ A simplest example:
 $ cat examples/train_log/mnist-convnet/stat.json \
         | jq '.[] | .train_error, .validation_error' \
         | paste - - \
-        | plot-point.py --legend 'train,val' --title 'error'
+        | plot-point.py --legend 'train,val' --xlabel 'epoch' --ylabel 'error'
 
 For more usage, see `plot-point.py -h` or the code.
 """
@@ -23,8 +23,9 @@ import argparse, sys
 from collections import defaultdict
 from itertools import chain
 
-#from matplotlib import rc
+from matplotlib import rc
 #rc('font',**{'family':'sans-serif','sans-serif':['Helvetica']})
+#rc('font',**{'family':'sans-serif','sans-serif':['Microsoft Yahei']})
 #rc('text', usetex=True)
 
 STDIN_FNAME = '-'
@@ -168,7 +169,7 @@ def plot_args_from_column_desc(desc):
 
 def do_plot(data_xs, data_ys):
     """
-    data_xs: list of 1d array, either of size 1 of size len(data_ys)
+    data_xs: list of 1d array, either of size 1 or size len(data_ys)
     data_ys: list of 1d array
     """
     fig = plt.figure(figsize = (16.18/1.2, 10/1.2))
@@ -214,8 +215,8 @@ def do_plot(data_xs, data_ys):
         if args.annotate_maximum or args.annotate_minimum:
             annotate_min_max(truncate_data_x, data_y, ax)
 
-    plt.xlabel(args.xlabel, fontsize='xx-large')
-    plt.ylabel(args.ylabel, fontsize='xx-large')
+    plt.xlabel(args.xlabel.decode('utf-8'), fontsize='xx-large')
+    plt.ylabel(args.ylabel.decode('utf-8'), fontsize='xx-large')
     plt.legend(loc='best', fontsize='xx-large')
 
     # adjust maxx
@@ -232,7 +233,7 @@ def do_plot(data_xs, data_ys):
     plt.title(args.title, fontdict={'fontsize': '20'})
 
     if args.output != '':
-        plt.savefig(args.output)
+        plt.savefig(args.output, bbox_inches='tight')
     if args.show:
         plt.show()
 

tensorpack/callbacks/inference.py

@@ -55,7 +55,7 @@ class Inferencer(object):
         """
         Return a list of tensor names needed for this inference
         """
-        return self._get_output_vars()
+        return self._get_output_tensors()
 
     @abstractmethod
     def _get_output_tensors(self):
@@ -66,18 +66,18 @@ class InferenceRunner(Callback):
     A callback that runs different kinds of inferencer.
     """
 
-    def __init__(self, ds, vcs):
+    def __init__(self, ds, infs):
         """
         :param ds: inference dataset. a `DataFlow` instance.
-        :param vcs: a list of `Inferencer` instance.
+        :param infs: a list of `Inferencer` instance.
         """
         assert isinstance(ds, DataFlow), type(ds)
         self.ds = ds
-        if not isinstance(vcs, list):
-            self.vcs = [vcs]
+        if not isinstance(infs, list):
+            self.infs = [infs]
         else:
-            self.vcs = vcs
-        for v in self.vcs:
+            self.infs = infs
+        for v in self.infs:
             assert isinstance(v, Inferencer), str(v)
 
     def _setup_graph(self):
@@ -89,21 +89,21 @@ class InferenceRunner(Callback):
 
     def _find_output_tensors(self):
         self.output_tensors = []    # list of names
-        self.vc_to_vars = []    # list of list of (var_name: output_idx)
-        for vc in self.vcs:
-            vc_vars = vc._get_output_tensors()
-            def find_oid(var):
-                if var in self.output_tensors:
-                    return self.output_tensors.index(var)
+        self.inf_to_tensors = []    # list of list of (var_name: output_idx)
+        for inf in self.infs:
+            inf_tensors = inf.get_output_tensors()
+            def find_oid(t):
+                if t in self.output_tensors:
+                    return self.output_tensors.index(t)
                 else:
-                    self.output_tensors.append(var)
+                    self.output_tensors.append(t)
                     return len(self.output_tensors) - 1
-            vc_vars = [(var, find_oid(var)) for var in vc_vars]
-            self.vc_to_vars.append(vc_vars)
+            inf_tensors = [(t, find_oid(t)) for t in inf_tensors]
+            self.inf_to_tensors.append(inf_tensors)
 
     def _trigger_epoch(self):
-        for vc in self.vcs:
-            vc.before_inference()
+        for inf in self.infs:
+            inf.before_inference()
 
         sess = tf.get_default_session()
         self.ds.reset_state()
@@ -112,18 +112,18 @@ class InferenceRunner(Callback):
                 #feed = dict(zip(self.input_vars, dp))   # TODO custom dp mapping?
                 #outputs = sess.run(self.output_tensors, feed_dict=feed)
                 outputs = self.pred_func(dp)
-                for vc, varsmap in zip(self.vcs, self.vc_to_vars):
-                    vc_output = [outputs[k[1]] for k in varsmap]
-                    vc.datapoint(dp, vc_output)
+                for inf, tensormap in zip(self.infs, self.inf_to_tensors):
+                    inf_output = [outputs[k[1]] for k in tensormap]
+                    inf.datapoint(dp, inf_output)
                 pbar.update()
 
-        for vc in self.vcs:
-            ret = vc.after_inference()
+        for inf in self.infs:
+            ret = inf.after_inference()
             for k, v in six.iteritems(ret):
                 try:
                     v = float(v)
                 except:
-                    logger.warn("{} returns a non-scalar statistics!".format(type(vc).__name__))
+                    logger.warn("{} returns a non-scalar statistics!".format(type(inf).__name__))
                     continue
                 self.trainer.write_scalar_summary(k, v)