X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=pysvrt.git;a=blobdiff_plain;f=cnn-svrt.py;h=966ea4a60262e71e7c6a5fd53347f9580058cb9b;hp=8baaacbc4fe7b4b37d8e6be27dd229b5b44bf6cc;hb=a25efec7618acd54806bd6ce69b30d473b7845f8;hpb=349b55a2d9ca213718df8941058d42689ba68163

diff --git a/cnn-svrt.py b/cnn-svrt.py
index 8baaacb..966ea4a 100755
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -24,8 +24,10 @@
 import time
 import argparse
 import math
+
 import distutils.util
 import re
+import signal
 
 from colorama import Fore, Back, Style
 
@@ -83,6 +85,9 @@ parser.add_argument('--compress_vignettes',
                     type = distutils.util.strtobool, default = 'True',
                     help = 'Use lossless compression to reduce the memory footprint')
 
+parser.add_argument('--save_test_mistakes',
+                    type = distutils.util.strtobool, default = 'False')
+
 parser.add_argument('--model',
                     type = str, default = 'deepnet',
                     help = 'What model to use')
@@ -127,7 +132,24 @@ def log_string(s, remark = ''):
     log_file.write(re.sub(' ', '_', time.ctime()) + ' ' + elapsed + ' ' + s + '\n')
     log_file.flush()
 
-    print(Fore.BLUE + time.ctime() + ' ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s + Fore.CYAN + remark + Style.RESET_ALL)
+    print(Fore.BLUE + time.ctime() + ' ' + Fore.GREEN + elapsed \
+          + Style.RESET_ALL
+          + ' ' \
+          + s + Fore.CYAN + remark \
+          + Style.RESET_ALL)
+
+######################################################################
+
+def handler_sigint(signum, frame):
+    log_string('got sigint')
+    exit(0)
+
+def handler_sigterm(signum, frame):
+    log_string('got sigterm')
+    exit(0)
+
+signal.signal(signal.SIGINT, handler_sigint)
+signal.signal(signal.SIGTERM, handler_sigterm)
 
 ######################################################################
 
@@ -319,7 +341,7 @@ class DeepNet3(nn.Module):
 
 ######################################################################
 
-def nb_errors(model, data_set):
+def nb_errors(model, data_set, mistake_filename_pattern = None):
     ne = 0
     for b in range(0, data_set.nb_batches):
         input, target = data_set.get_batch(b)
@@ -329,7 +351,14 @@ def nb_errors(model, data_set):
         for i in range(0, data_set.batch_size):
             if wta_prediction[i] != target[i]:
                 ne = ne + 1
-
+                if mistake_filename_pattern is not None:
+                    img = input[i].clone()
+                    img.sub_(img.min())
+                    img.div_(img.max())
+                    k = b * data_set.batch_size + i
+                    filename = mistake_filename_pattern.format(k, target[i])
+                    torchvision.utils.save_image(img, filename)
+                    print(Fore.RED + 'Wrote ' + filename + Style.RESET_ALL)
     return ne
 
 ######################################################################
@@ -531,7 +560,8 @@ for problem_number in map(int, args.problems.split(',')):
                                args.nb_test_samples, args.batch_size,
                                cuda = torch.cuda.is_available())
 
-        nb_test_errors = nb_errors(model, test_set)
+        nb_test_errors = nb_errors(model, test_set,
+                                   mistake_filename_pattern = 'mistake_{:06d}_{:d}.png')
 
         log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(
             problem_number,