X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=cnn-svrt.py;h=e5ecf768bfdc060b255c6612649f3f2ede62a51a;hb=9eec6d457d017e0204cc80c0e1b24f894d064267;hp=cfef09da934fb5d82181715f428dab1a261d04ba;hpb=37030c396217cfe89f8dfa2b9e10ff1ec783a5a7;p=pysvrt.git
diff --git a/cnn-svrt.py b/cnn-svrt.py
index cfef09d..e5ecf76 100755
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -19,11 +19,12 @@
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
-# along with selector. If not, see .
+# along with svrt. If not, see .
import time
import argparse
import math
+import distutils.util
from colorama import Fore, Back, Style
@@ -40,7 +41,7 @@ from torchvision import datasets, transforms, utils
# SVRT
-import vignette_set
+import svrtset
######################################################################
@@ -65,22 +66,26 @@ parser.add_argument('--log_file',
type = str, default = 'default.log')
parser.add_argument('--compress_vignettes',
- action='store_true', default = True,
+ type = distutils.util.strtobool, default = 'True',
help = 'Use lossless compression to reduce the memory footprint')
parser.add_argument('--deep_model',
- action='store_true', default = True,
+ type = distutils.util.strtobool, default = 'True',
help = 'Use Afroze\'s Alexnet-like deep model')
parser.add_argument('--test_loaded_models',
- action='store_true', default = False,
+ type = distutils.util.strtobool, default = 'False',
help = 'Should we compute the test errors of loaded models')
+parser.add_argument('--problems',
+ type = str, default = '1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23',
+ help = 'What problems to process')
+
args = parser.parse_args()
######################################################################
-log_file = open(args.log_file, 'w')
+log_file = open(args.log_file, 'a')
pred_log_t = None
print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL)
@@ -143,22 +148,6 @@ class AfrozeShallowNet(nn.Module):
# Afroze's DeepNet
-# map size nb. maps
-# ----------------------
-# input 128x128 1
-# -- conv(21x21 x 32 stride=4) -> 28x28 32
-# -- max(2x2) -> 14x14 6
-# -- conv(7x7 x 96) -> 8x8 16
-# -- max(2x2) -> 4x4 16
-# -- conv(5x5 x 96) -> 26x36 16
-# -- conv(3x3 x 128) -> 36x36 16
-# -- conv(3x3 x 128) -> 36x36 16
-
-# -- conv(5x5 x 120) -> 1x1 120
-# -- reshape -> 120 1
-# -- full(3x84) -> 84 1
-# -- full(84x2) -> 2 1
-
class AfrozeDeepNet(nn.Module):
def __init__(self):
super(AfrozeDeepNet, self).__init__()
@@ -262,18 +251,38 @@ def int_to_suffix(n):
else:
return str(n)
+class vignette_logger():
+ def __init__(self, delay_min = 60):
+ self.start_t = time.time()
+ self.last_t = self.start_t
+ self.delay_min = delay_min
+
+ def __call__(self, n, m):
+ t = time.time()
+ if t > self.last_t + self.delay_min:
+ dt = (t - self.start_t) / m
+ log_string('sample_generation {:d} / {:d}'.format(
+ m,
+ n), ' [ETA ' + time.ctime(time.time() + dt * (n - m)) + ']'
+ )
+ self.last_t = t
+
######################################################################
if args.nb_train_samples%args.batch_size > 0 or args.nb_test_samples%args.batch_size > 0:
print('The number of samples must be a multiple of the batch size.')
raise
+log_string('############### start ###############')
+
if args.compress_vignettes:
- VignetteSet = vignette_set.CompressedVignetteSet
+ log_string('using_compressed_vignettes')
+ VignetteSet = svrtset.CompressedVignetteSet
else:
- VignetteSet = vignette_set.VignetteSet
+ log_string('using_uncompressed_vignettes')
+ VignetteSet = svrtset.VignetteSet
-for problem_number in range(1, 24):
+for problem_number in map(int, args.problems.split(',')):
log_string('############### problem ' + str(problem_number) + ' ###############')
@@ -313,7 +322,8 @@ for problem_number in range(1, 24):
train_set = VignetteSet(problem_number,
args.nb_train_samples, args.batch_size,
- cuda = torch.cuda.is_available())
+ cuda = torch.cuda.is_available(),
+ logger = vignette_logger())
log_string('data_generation {:0.2f} samples / s'.format(
train_set.nb_samples / (time.time() - t))