# General Public License for more details.
#
# You should have received a copy of the GNU General Public License
-# along with selector. If not, see <http://www.gnu.org/licenses/>.
+# along with svrt. If not, see <http://www.gnu.org/licenses/>.
import time
import argparse
import math
+import distutils.util
from colorama import Fore, Back, Style
# SVRT
-from vignette_set import VignetteSet, CompressedVignetteSet
+import svrtset
######################################################################
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()
######################################################################
# 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__()
######################################################################
def int_to_suffix(n):
- if n > 1000000 and n%1000000 == 0:
+ if n >= 1000000 and n%1000000 == 0:
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)
+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
-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
- log_string('**** problem ' + str(problem_number) + ' ****')
+for problem_number in map(int, args.problems.split(',')):
+
+ log_string('############### problem ' + str(problem_number) + ' ###############')
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))
+ ##################################################
+ # Tries to load the model
+
need_to_train = False
try:
model.load_state_dict(torch.load(model_filename))
except:
need_to_train = True
+ ##################################################
+ # Train if necessary
+
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))
train_set.nb_samples)
)
+ ##################################################
+ # Test if necessary
+
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))
projects / pysvrt.git / blobdiff