# along with selector. If not, see <http://www.gnu.org/licenses/>.
import time
+import argparse
+
+from colorama import Fore, Back, Style
import torch
######################################################################
+parser = argparse.ArgumentParser(
+ description = 'Simple convnet test on the SVRT.',
+ formatter_class = argparse.ArgumentDefaultsHelpFormatter
+)
+
+parser.add_argument('--nb_train_samples',
+ type = int, default = 100000,
+ help = 'How many samples for train')
+
+parser.add_argument('--nb_test_samples',
+ type = int, default = 10000,
+ help = 'How many samples for test')
+
+parser.add_argument('--nb_epochs',
+ type = int, default = 100,
+ help = 'How many training epochs')
+
+parser.add_argument('--log_file',
+ type = str, default = 'cnn-svrt.log',
+ help = 'Log file name')
+
+args = parser.parse_args()
+
+######################################################################
+
+log_file = open(args.log_file, 'w')
+
+print(Fore.RED + 'Logging into ' + args.log_file + Style.RESET_ALL)
+
+def log_string(s):
+ s = Fore.GREEN + time.ctime() + Style.RESET_ALL + ' ' + s
+ log_file.write(s + '\n')
+ log_file.flush()
+ print(s)
+
+######################################################################
+
def generate_set(p, n):
target = torch.LongTensor(n).bernoulli_(0.5)
+ t = time.time()
input = svrt.generate_vignettes(p, target)
+ t = time.time() - t
+ log_string('DATA_SET_GENERATION {:.02f} sample/s'.format(n / t))
input = input.view(input.size(0), 1, input.size(1), input.size(2)).float()
return Variable(input), Variable(target)
######################################################################
-# 128x128 --conv(9)-> 120x120 --max(4)-> 30x30 --conv(6)-> 25x25 --max(5)-> 5x5
+# Afroze's ShallowNet
+
+# map size nb. maps
+# ----------------------
+# input 128x128 1
+# -- conv(21x21 x 6) -> 108x108 6
+# -- max(2x2) -> 54x54 6
+# -- conv(19x19 x 16) -> 36x36 16
+# -- max(2x2) -> 18x18 16
+# -- conv(18x18 x 120) -> 1x1 120
+# -- reshape -> 120 1
+# -- full(120x84) -> 84 1
+# -- full(84x2) -> 2 1
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
- self.conv1 = nn.Conv2d(1, 10, kernel_size=9)
- self.conv2 = nn.Conv2d(10, 20, kernel_size=6)
- self.fc1 = nn.Linear(500, 100)
- self.fc2 = nn.Linear(100, 2)
+ self.conv1 = nn.Conv2d(1, 6, kernel_size=21)
+ self.conv2 = nn.Conv2d(6, 16, kernel_size=19)
+ self.conv3 = nn.Conv2d(16, 120, kernel_size=18)
+ self.fc1 = nn.Linear(120, 84)
+ self.fc2 = nn.Linear(84, 2)
def forward(self, x):
- x = fn.relu(fn.max_pool2d(self.conv1(x), kernel_size=4, stride=4))
- x = fn.relu(fn.max_pool2d(self.conv2(x), kernel_size=5, stride=5))
- x = x.view(-1, 500)
+ x = fn.relu(fn.max_pool2d(self.conv1(x), kernel_size=2))
+ x = fn.relu(fn.max_pool2d(self.conv2(x), kernel_size=2))
+ x = fn.relu(self.conv3(x))
+ x = x.view(-1, 120)
x = fn.relu(self.fc1(x))
x = self.fc2(x)
return x
def train_model(train_input, train_target):
model, criterion = Net(), nn.CrossEntropyLoss()
+ nb_parameters = 0
+ for p in model.parameters():
+ nb_parameters += p.numel()
+ log_string('NB_PARAMETERS {:d}'.format(nb_parameters))
+
if torch.cuda.is_available():
model.cuda()
criterion.cuda()
- nb_epochs = 25
- optimizer, bs = optim.SGD(model.parameters(), lr = 1e-1), 100
+ optimizer, bs = optim.SGD(model.parameters(), lr = 1e-2), 100
- for k in range(0, nb_epochs):
- for b in range(0, nb_train_samples, bs):
+ for k in range(0, args.nb_epochs):
+ acc_loss = 0.0
+ for b in range(0, train_input.size(0), bs):
output = model.forward(train_input.narrow(0, b, bs))
loss = criterion(output, train_target.narrow(0, b, bs))
+ acc_loss = acc_loss + loss.data[0]
model.zero_grad()
loss.backward()
optimizer.step()
+ log_string('TRAIN_LOSS {:d} {:f}'.format(k, acc_loss))
return model
######################################################################
-def print_test_error(model, test_input, test_target):
- bs = 100
- nb_test_errors = 0
+def nb_errors(model, data_input, data_target, bs = 100):
+ ne = 0
- for b in range(0, nb_test_samples, bs):
- output = model.forward(test_input.narrow(0, b, bs))
+ for b in range(0, data_input.size(0), bs):
+ output = model.forward(data_input.narrow(0, b, bs))
wta_prediction = output.data.max(1)[1].view(-1)
for i in range(0, bs):
- if wta_prediction[i] != test_target.narrow(0, b, bs).data[i]:
- nb_test_errors = nb_test_errors + 1
+ if wta_prediction[i] != data_target.narrow(0, b, bs).data[i]:
+ ne = ne + 1
- print('TEST_ERROR {:.02f}% ({:d}/{:d})'.format(
- 100 * nb_test_errors / nb_test_samples,
- nb_test_errors,
- nb_test_samples)
- )
+ return ne
######################################################################
-nb_train_samples = 100000
-nb_test_samples = 10000
+for arg in vars(args):
+ log_string('ARGUMENT ' + str(arg) + ' ' + str(getattr(args, arg)))
-for p in range(1, 24):
- print('-- PROBLEM #{:d} --'.format(p))
-
- t1 = time.time()
- train_input, train_target = generate_set(p, nb_train_samples)
- test_input, test_target = generate_set(p, nb_test_samples)
+for problem_number in range(1, 24):
+ train_input, train_target = generate_set(problem_number, args.nb_train_samples)
+ test_input, test_target = generate_set(problem_number, args.nb_test_samples)
if torch.cuda.is_available():
train_input, train_target = train_input.cuda(), train_target.cuda()
train_input.data.sub_(mu).div_(std)
test_input.data.sub_(mu).div_(std)
- t2 = time.time()
- print('[data generation {:.02f}s]'.format(t2 - t1))
model = train_model(train_input, train_target)
- t3 = time.time()
- print('[train {:.02f}s]'.format(t3 - t2))
- print_test_error(model, test_input, test_target)
+ nb_train_errors = nb_errors(model, train_input, train_target)
+
+ log_string('TRAIN_ERROR {:d} {:.02f}% {:d} {:d}'.format(
+ problem_number,
+ 100 * nb_train_errors / train_input.size(0),
+ nb_train_errors,
+ train_input.size(0))
+ )
- t4 = time.time()
+ nb_test_errors = nb_errors(model, test_input, test_target)
- print('[test {:.02f}s]'.format(t4 - t3))
- print()
+ log_string('TEST_ERROR {:d} {:.02f}% {:d} {:d}'.format(
+ problem_number,
+ 100 * nb_test_errors / test_input.size(0),
+ nb_test_errors,
+ test_input.size(0))
+ )
######################################################################
projects / pysvrt.git / blobdiff