import time
import argparse
import math
+import distutils.util
from colorama import Fore, Back, Style
# SVRT
-from vignette_set import VignetteSet, CompressedVignetteSet
+import vignette_set
######################################################################
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',
- type = int, default = 100000,
- help = 'How many samples for train')
+ type = int, default = 100000)
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',
- type = int, default = 50,
- help = 'How many training epochs')
+ type = int, default = 50)
parser.add_argument('--batch_size',
- type = int, default = 100,
- help = 'Mini-batch size')
+ type = int, default = 100)
parser.add_argument('--log_file',
- type = str, default = 'default.log',
- help = 'Log file name')
+ type = str, default = 'default.log')
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',
- 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',
- action='store_true', default = False,
+ type = distutils.util.strtobool, default = 'False',
help = 'Should we compute the test errors of loaded models')
args = parser.parse_args()
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()
- 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)
######################################################################
######################################################################
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)
print('The number of samples must be a multiple of the batch size.')
raise
+if args.compress_vignettes:
+ log_string('using_compressed_vignettes')
+ VignetteSet = vignette_set.CompressedVignetteSet
+else:
+ log_string('using_uncompressed_vignettes')
+ VignetteSet = vignette_set.VignetteSet
+
for problem_number in range(1, 24):
- log_string('**** problem ' + str(problem_number) + ' ****')
+ 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())
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))