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Minor change.
[pysvrt.git]
/
cnn-svrt.py
diff --git
a/cnn-svrt.py
b/cnn-svrt.py
index
cc3d35f
..
63b11ee
100755
(executable)
--- 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
# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
-# along with s
elector
. If not, see <http://www.gnu.org/licenses/>.
+# along with s
vrt
. If not, see <http://www.gnu.org/licenses/>.
import time
import argparse
import math
import time
import argparse
import math
+import distutils.util
from colorama import Fore, Back, Style
from colorama import Fore, Back, Style
@@
-40,47
+41,46
@@
from torchvision import datasets, transforms, utils
# SVRT
# SVRT
-
from vignette_set import VignetteSet, CompressedVignetteS
et
+
import svrts
et
######################################################################
parser = argparse.ArgumentParser(
######################################################################
parser = argparse.ArgumentParser(
- description =
'Simple convnet test on the SVRT.'
,
+ description =
"Convolutional networks for the SVRT. Written by Francois Fleuret, (C) Idiap research institute."
,
formatter_class = argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--nb_train_samples',
formatter_class = argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument('--nb_train_samples',
- type = int, default = 100000,
- help = 'How many samples for train')
+ type = int, default = 100000)
parser.add_argument('--nb_test_samples',
parser.add_argument('--nb_test_samples',
- type = int, default = 10000,
- help = 'How many samples for test')
+ type = int, default = 10000)
parser.add_argument('--nb_epochs',
parser.add_argument('--nb_epochs',
- type = int, default = 50,
- help = 'How many training epochs')
+ type = int, default = 50)
parser.add_argument('--batch_size',
parser.add_argument('--batch_size',
- type = int, default = 100,
- help = 'Mini-batch size')
+ type = int, default = 100)
parser.add_argument('--log_file',
parser.add_argument('--log_file',
- type = str, default = 'default.log',
- help = 'Log file name')
+ type = str, default = 'default.log')
parser.add_argument('--compress_vignettes',
parser.add_argument('--compress_vignettes',
-
action='store_true', default = False
,
+
type = distutils.util.strtobool, default = 'True'
,
help = 'Use lossless compression to reduce the memory footprint')
parser.add_argument('--deep_model',
help = 'Use lossless compression to reduce the memory footprint')
parser.add_argument('--deep_model',
-
action='store_true', default = False
,
+
type = distutils.util.strtobool, default = 'True'
,
help = 'Use Afroze\'s Alexnet-like deep model')
parser.add_argument('--test_loaded_models',
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')
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()
######################################################################
args = parser.parse_args()
######################################################################
@@
-104,10
+104,10
@@
def log_string(s, remark = ''):
pred_log_t = t
pred_log_t = t
- s = Fore.BLUE + '[' + time.ctime() + '] ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s
- log_file.write(s + '\n')
+ log_file.write('[' + time.ctime() + '] ' + elapsed + ' ' + s + '\n')
log_file.flush()
log_file.flush()
- print(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)
######################################################################
######################################################################
@@
-148,22
+148,6
@@
class AfrozeShallowNet(nn.Module):
# Afroze's DeepNet
# 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__()
class AfrozeDeepNet(nn.Module):
def __init__(self):
super(AfrozeDeepNet, self).__init__()
@@
-260,39
+244,64
@@
for arg in vars(args):
######################################################################
def int_to_suffix(n):
######################################################################
def int_to_suffix(n):
- if n > 1000000 and n%1000000 == 0:
+ if n >
=
1000000 and n%1000000 == 0:
return str(n//1000000) + 'M'
return str(n//1000000) + 'M'
- elif n > 1000 and n%1000 == 0:
+ elif n >
=
1000 and n%1000 == 0:
return str(n//1000) + 'K'
else:
return str(n)
return str(n//1000) + 'K'
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
######################################################################
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
-for problem_number in range(1, 24):
+if args.compress_vignettes:
+ log_string('using_compressed_vignettes')
+ VignetteSet = svrtset.CompressedVignetteSet
+else:
+ log_string('using_uncompressed_vignettes')
+ VignetteSet = svrtset.VignetteSet
+
+for problem_number in map(int, args.problems.split(',')):
- log_string('
**** problem ' + str(problem_number) + ' ****
')
+ log_string('
############### problem ' + str(problem_number) + ' ###############
')
if args.deep_model:
model = AfrozeDeepNet()
else:
model = AfrozeShallowNet()
if args.deep_model:
model = AfrozeDeepNet()
else:
model = AfrozeShallowNet()
- if torch.cuda.is_available():
- model.cuda()
+ if torch.cuda.is_available(): model.cuda()
- model_filename = model.name + '_' + \
- str(problem_number) + '_' + \
+ model_filename = model.name + '_
pb:
' + \
+ str(problem_number) + '_
ns:
' + \
int_to_suffix(args.nb_train_samples) + '.param'
nb_parameters = 0
for p in model.parameters(): nb_parameters += p.numel()
log_string('nb_parameters {:d}'.format(nb_parameters))
int_to_suffix(args.nb_train_samples) + '.param'
nb_parameters = 0
for p in model.parameters(): nb_parameters += p.numel()
log_string('nb_parameters {:d}'.format(nb_parameters))
+ ##################################################
+ # Tries to load the model
+
need_to_train = False
try:
model.load_state_dict(torch.load(model_filename))
need_to_train = False
try:
model.load_state_dict(torch.load(model_filename))
@@
-300,20
+309,19
@@
for problem_number in range(1, 24):
except:
need_to_train = True
except:
need_to_train = True
+ ##################################################
+ # Train if necessary
+
if need_to_train:
log_string('training_model ' + model_filename)
t = time.time()
if need_to_train:
log_string('training_model ' + model_filename)
t = time.time()
- if args.compress_vignettes:
- train_set = CompressedVignetteSet(problem_number,
- args.nb_train_samples, args.batch_size,
- cuda = torch.cuda.is_available())
- else:
- train_set = VignetteSet(problem_number,
- args.nb_train_samples, args.batch_size,
- cuda = torch.cuda.is_available())
+ train_set = VignetteSet(problem_number,
+ args.nb_train_samples, args.batch_size,
+ cuda = torch.cuda.is_available(),
+ logger = vignette_logger())
log_string('data_generation {:0.2f} samples / s'.format(
train_set.nb_samples / (time.time() - t))
log_string('data_generation {:0.2f} samples / s'.format(
train_set.nb_samples / (time.time() - t))
@@
-332,18
+340,16
@@
for problem_number in range(1, 24):
train_set.nb_samples)
)
train_set.nb_samples)
)
+ ##################################################
+ # Test if necessary
+
if need_to_train or args.test_loaded_models:
t = time.time()
if need_to_train or args.test_loaded_models:
t = time.time()
- if args.compress_vignettes:
- test_set = CompressedVignetteSet(problem_number,
- args.nb_test_samples, args.batch_size,
- cuda = torch.cuda.is_available())
- else:
- test_set = VignetteSet(problem_number,
- args.nb_test_samples, args.batch_size,
- cuda = torch.cuda.is_available())
+ test_set = VignetteSet(problem_number,
+ args.nb_test_samples, args.batch_size,
+ cuda = torch.cuda.is_available())
log_string('data_generation {:0.2f} samples / s'.format(
test_set.nb_samples / (time.time() - t))
log_string('data_generation {:0.2f} samples / s'.format(
test_set.nb_samples / (time.time() - t))