X-Git-Url: https://www.fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=blobdiff_plain;f=cnn-svrt.py;h=f3d350eb9ea9203f408a0603e7c0458b88801e95;hb=212e14ec93489fcaa8a039c4bc64abeb8852c5ec;hp=0216b4e5cbba5740e053d43d5d9fad5ff0ab2f27;hpb=f18f927a8bb37ca1c43bf6cf38237c274944753a;p=pysvrt.git
diff --git a/cnn-svrt.py b/cnn-svrt.py
index 0216b4e..f3d350e 100755
--- a/cnn-svrt.py
+++ b/cnn-svrt.py
@@ -19,7 +19,7 @@
# 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
@@ -41,7 +41,7 @@ from torchvision import datasets, transforms, utils
# SVRT
-import vignette_set
+import svrtset
######################################################################
@@ -56,6 +56,13 @@ parser.add_argument('--nb_train_samples',
parser.add_argument('--nb_test_samples',
type = int, default = 10000)
+parser.add_argument('--nb_validation_samples',
+ type = int, default = 10000)
+
+parser.add_argument('--validation_error_threshold',
+ type = float, default = 0.0,
+ help = 'Early training termination criterion')
+
parser.add_argument('--nb_epochs',
type = int, default = 50)
@@ -77,11 +84,15 @@ parser.add_argument('--test_loaded_models',
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)
@@ -144,22 +155,6 @@ 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__()
@@ -206,7 +201,22 @@ class AfrozeDeepNet(nn.Module):
######################################################################
-def train_model(model, train_set):
+def nb_errors(model, data_set):
+ ne = 0
+ for b in range(0, data_set.nb_batches):
+ input, target = data_set.get_batch(b)
+ output = model.forward(Variable(input))
+ wta_prediction = output.data.max(1)[1].view(-1)
+
+ for i in range(0, data_set.batch_size):
+ if wta_prediction[i] != target[i]:
+ ne = ne + 1
+
+ return ne
+
+######################################################################
+
+def train_model(model, train_set, validation_set):
batch_size = args.batch_size
criterion = nn.CrossEntropyLoss()
@@ -228,25 +238,24 @@ def train_model(model, train_set):
loss.backward()
optimizer.step()
dt = (time.time() - start_t) / (e + 1)
+
log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss),
' [ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + ']')
- return model
-
-######################################################################
+ if validation_set is not None:
+ nb_validation_errors = nb_errors(model, validation_set)
-def nb_errors(model, data_set):
- ne = 0
- for b in range(0, data_set.nb_batches):
- input, target = data_set.get_batch(b)
- output = model.forward(Variable(input))
- wta_prediction = output.data.max(1)[1].view(-1)
+ log_string('validation_error {:.02f}% {:d} {:d}'.format(
+ 100 * nb_validation_errors / validation_set.nb_samples,
+ nb_validation_errors,
+ validation_set.nb_samples)
+ )
- for i in range(0, data_set.batch_size):
- if wta_prediction[i] != target[i]:
- ne = ne + 1
+ if nb_validation_errors / validation_set.nb_samples <= args.validation_error_threshold:
+ log_string('below validation_error_threshold')
+ break
- return ne
+ return model
######################################################################
@@ -263,20 +272,38 @@ 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:
log_string('using_compressed_vignettes')
- VignetteSet = vignette_set.CompressedVignetteSet
+ VignetteSet = svrtset.CompressedVignetteSet
else:
log_string('using_uncompressed_vignettes')
- VignetteSet = vignette_set.VignetteSet
+ 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) + ' ###############')
@@ -316,13 +343,22 @@ 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))
)
- train_model(model, train_set)
+ if args.validation_error_threshold > 0.0:
+ validation_set = VignetteSet(problem_number,
+ args.nb_validation_samples, args.batch_size,
+ cuda = torch.cuda.is_available(),
+ logger = vignette_logger())
+ else:
+ validation_set = None
+
+ train_model(model, train_set, validation_set)
torch.save(model.state_dict(), model_filename)
log_string('saved_model ' + model_filename)
@@ -346,10 +382,6 @@ for problem_number in range(1, 24):
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))
- )
-
nb_test_errors = nb_errors(model, test_set)
log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(