add resnet curve

examples/ResNet/README.md

@@ -10,6 +10,9 @@ More results to come.
 | ResNet 18          |      10.67% |      29.50% |
 | ResNet 34          |      8.66%  |      26.45% |
 | ResNet 50          |      7.13%  |      24.12% |
+| ResNet 101         |      6.54%  |      22.89% |
+
+![imagenet](imagenet-resnet.png)
 
 ## load-resnet.py
 

scripts/plot-point.py

@@ -56,6 +56,10 @@ def get_args():
             help='x label', type=six.text_type)
     parser.add_argument('--ylabel',
             help='y label', type=six.text_type)
+    parser.add_argument('--xlim',
+            help='x lim', type=float, nargs=2)
+    parser.add_argument('--ylim',
+            help='y lim', type=float, nargs=2)
     parser.add_argument('-s', '--scale',
             help='scale of each y, separated by comma')
     parser.add_argument('--annotate-maximum',
@@ -218,6 +222,10 @@ def do_plot(data_xs, data_ys):
         plt.xlabel(args.xlabel, fontsize='xx-large')
     if args.ylabel:
         plt.ylabel(args.ylabel, fontsize='xx-large')
+    if args.xlim:
+        plt.xlim(args.xlim[0], args.xlim[1])
+    if args.ylim:
+        plt.ylim(args.ylim[0], args.ylim[1])
     plt.legend(loc='best', fontsize='xx-large')
 
     # adjust maxx
@@ -250,7 +258,7 @@ def main():
         fin.close()
 
     # parse column format
-    nr_column = len(all_inputs[0].rstrip().split(args.delimeter))
+    nr_column = len(all_inputs[0].rstrip('\n').split(args.delimeter))
     if args.column is None:
         column = ['y'] * nr_column
     else:
@@ -287,7 +295,9 @@ Line: {}""".format(repr(args.delimeter), line)
                 data[idx].append(val)
 
     data_ys = [data[k] for k in args.y_column_idx]
-    max_ysize = max([len(t) for t in data_ys])
+    length_ys = [len(t) for t in data_ys]
+    print("Length of each column:", length_ys)
+    max_ysize = max(length_ys)
     print("Size of the longest y column: ", max_ysize)
 
     if nr_x_column:

tensorpack/predict/dataset.py

@@ -60,7 +60,7 @@ class SimpleDatasetPredictor(DatasetPredictorBase):
             sz = self.dataset.size()
         except NotImplementedError:
             sz = 0
-        with tqdm(total=sz) as pbar:
+        with tqdm(total=sz, disable=(sz==0)) as pbar:
             for dp in self.dataset.get_data():
                 res = self.predictor(dp)
                 yield res
@@ -119,7 +119,7 @@ class MultiProcessDatasetPredictor(DatasetPredictorBase):
             sz = self.dataset.size()
         except NotImplementedError:
             sz = 0
-        with tqdm(total=sz) as pbar:
+        with tqdm(total=sz, disable=(sz==0)) as pbar:
             die_cnt = 0
             while True:
                 res = self.result_queue.get()