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Added --save_test_mistakes.
author
Francois Fleuret
<francois@fleuret.org>
Mon, 26 Jun 2017 13:40:52 +0000
(15:40 +0200)
committer
Francois Fleuret
<francois@fleuret.org>
Mon, 26 Jun 2017 13:40:52 +0000
(15:40 +0200)
cnn-svrt.py
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diff --git
a/cnn-svrt.py
b/cnn-svrt.py
index
ade87ce
..
3fe50d8
100755
(executable)
--- a/
cnn-svrt.py
+++ b/
cnn-svrt.py
@@
-85,6
+85,9
@@
parser.add_argument('--compress_vignettes',
type = distutils.util.strtobool, default = 'True',
help = 'Use lossless compression to reduce the memory footprint')
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')
parser.add_argument('--model',
type = str, default = 'deepnet',
help = 'What model to use')
@@
-338,7
+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)
ne = 0
for b in range(0, data_set.nb_batches):
input, target = data_set.get_batch(b)
@@
-348,6
+351,12
@@
def nb_errors(model, data_set):
for i in range(0, data_set.batch_size):
if wta_prediction[i] != target[i]:
ne = ne + 1
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())
+ torchvision.utils.save_image(img,
+ mistake_filename_pattern.format(b + i, target[i]))
return ne
return ne
@@
-550,7
+559,8
@@
for problem_number in map(int, args.problems.split(',')):
args.nb_test_samples, args.batch_size,
cuda = torch.cuda.is_available())
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_{:d}_{:06d}.png')
log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(
problem_number,
log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(
problem_number,