# 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
+import re
+import signal
from colorama import Fore, Back, Style
+# Pytorch
+
import torch
+import torchvision
from torch import optim
+from torch import multiprocessing
from torch import FloatTensor as Tensor
from torch.autograd import Variable
from torch import nn
from torch.nn import functional as fn
+
from torchvision import datasets, transforms, utils
-import svrt
+# SVRT
+
+import svrtset
######################################################################
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_batches',
- type = int, default = 1000,
- help = 'How many samples for train')
+parser.add_argument('--nb_train_samples',
+ type = int, default = 100000)
-parser.add_argument('--nb_test_batches',
- type = int, default = 100,
- help = 'How many samples for test')
+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,
- 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 = 'cnn-svrt.log',
- help = 'Log file name')
+ type = str, default = 'default.log')
+
+parser.add_argument('--nb_exemplar_vignettes',
+ type = int, default = 32)
+
+parser.add_argument('--compress_vignettes',
+ type = distutils.util.strtobool, default = 'True',
+ help = 'Use lossless compression to reduce the memory footprint')
+
+parser.add_argument('--save_test_mistakes',
+ type = distutils.util.strtobool, default = 'False')
+
+parser.add_argument('--model',
+ type = str, default = 'deepnet',
+ help = 'What model to use')
+
+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')
+log_file.write('\n')
+log_file.write('@@@@@@@@@@@@@@@@@@@ ' + time.ctime() + ' @@@@@@@@@@@@@@@@@@@\n')
+log_file.write('\n')
+
+pred_log_t = None
+last_tag_t = time.time()
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 and prints the string, with a time stamp. Does not log the
+# remark
+
+def log_string(s, remark = ''):
+ global pred_log_t, last_tag_t
+
+ t = time.time()
+
+ if pred_log_t is None:
+ elapsed = 'start'
+ else:
+ elapsed = '+{:.02f}s'.format(t - pred_log_t)
+
+ pred_log_t = t
+
+ if t > last_tag_t + 3600:
+ last_tag_t = t
+ print(Fore.RED + time.ctime() + Style.RESET_ALL)
+
+ log_file.write(re.sub(' ', '_', time.ctime()) + ' ' + elapsed + ' ' + s + '\n')
log_file.flush()
- print(s)
+
+ print(Fore.BLUE + time.ctime() + ' ' + Fore.GREEN + elapsed \
+ + Style.RESET_ALL
+ + ' ' \
+ + s + Fore.CYAN + remark \
+ + Style.RESET_ALL)
######################################################################
-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)
+def handler_sigint(signum, frame):
+ log_string('got sigint')
+ exit(0)
+
+def handler_sigterm(signum, frame):
+ log_string('got sigterm')
+ exit(0)
+
+signal.signal(signal.SIGINT, handler_sigint)
+signal.signal(signal.SIGTERM, handler_sigterm)
######################################################################
# -- full(84x2) -> 2 1
class AfrozeShallowNet(nn.Module):
+ name = 'shallownet'
+
def __init__(self):
super(AfrozeShallowNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, kernel_size=21)
x = self.fc2(x)
return x
-def train_model(model, train_input, train_target):
- bs = args.batch_size
+######################################################################
+
+# Afroze's DeepNet
+
+class AfrozeDeepNet(nn.Module):
+
+ name = 'deepnet'
+
+ def __init__(self):
+ super(AfrozeDeepNet, self).__init__()
+ self.conv1 = nn.Conv2d( 1, 32, kernel_size=7, stride=4, padding=3)
+ self.conv2 = nn.Conv2d( 32, 96, kernel_size=5, padding=2)
+ self.conv3 = nn.Conv2d( 96, 128, kernel_size=3, padding=1)
+ self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+ self.conv5 = nn.Conv2d(128, 96, kernel_size=3, padding=1)
+ self.fc1 = nn.Linear(1536, 256)
+ self.fc2 = nn.Linear(256, 256)
+ self.fc3 = nn.Linear(256, 2)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = self.conv2(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = self.conv3(x)
+ x = fn.relu(x)
+
+ x = self.conv4(x)
+ x = fn.relu(x)
+
+ x = self.conv5(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = x.view(-1, 1536)
+
+ x = self.fc1(x)
+ x = fn.relu(x)
+
+ x = self.fc2(x)
+ x = fn.relu(x)
+
+ x = self.fc3(x)
+
+ return x
+
+######################################################################
+
+class DeepNet2(nn.Module):
+ name = 'deepnet2'
+
+ def __init__(self):
+ super(DeepNet2, self).__init__()
+ self.nb_channels = 512
+ self.conv1 = nn.Conv2d( 1, 32, kernel_size=7, stride=4, padding=3)
+ self.conv2 = nn.Conv2d( 32, self.nb_channels, kernel_size=5, padding=2)
+ self.conv3 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+ self.conv4 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+ self.conv5 = nn.Conv2d(self.nb_channels, self.nb_channels, kernel_size=3, padding=1)
+ self.fc1 = nn.Linear(16 * self.nb_channels, 512)
+ self.fc2 = nn.Linear(512, 512)
+ self.fc3 = nn.Linear(512, 2)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = self.conv2(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = self.conv3(x)
+ x = fn.relu(x)
+
+ x = self.conv4(x)
+ x = fn.relu(x)
+
+ x = self.conv5(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = x.view(-1, 16 * self.nb_channels)
+
+ x = self.fc1(x)
+ x = fn.relu(x)
+
+ x = self.fc2(x)
+ x = fn.relu(x)
+
+ x = self.fc3(x)
+
+ return x
+
+######################################################################
+
+class DeepNet3(nn.Module):
+ name = 'deepnet3'
+
+ def __init__(self):
+ super(DeepNet3, self).__init__()
+ self.conv1 = nn.Conv2d( 1, 32, kernel_size=7, stride=4, padding=3)
+ self.conv2 = nn.Conv2d( 32, 128, kernel_size=5, padding=2)
+ self.conv3 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+ self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+ self.conv5 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+ self.conv6 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+ self.conv7 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
+ self.fc1 = nn.Linear(2048, 256)
+ self.fc2 = nn.Linear(256, 256)
+ self.fc3 = nn.Linear(256, 2)
+
+ def forward(self, x):
+ x = self.conv1(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = self.conv2(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = self.conv3(x)
+ x = fn.relu(x)
+
+ x = self.conv4(x)
+ x = fn.relu(x)
+
+ x = self.conv5(x)
+ x = fn.max_pool2d(x, kernel_size=2)
+ x = fn.relu(x)
+
+ x = self.conv6(x)
+ x = fn.relu(x)
+
+ x = self.conv7(x)
+ x = fn.relu(x)
+
+ x = x.view(-1, 2048)
+
+ x = self.fc1(x)
+ x = fn.relu(x)
+
+ x = self.fc2(x)
+ x = fn.relu(x)
+
+ x = self.fc3(x)
+
+ return x
+
+######################################################################
+
+def nb_errors(model, data_set, mistake_filename_pattern = None):
+ 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
+ if mistake_filename_pattern is not None:
+ img = input[i].clone()
+ img.sub_(img.min())
+ img.div_(img.max())
+ k = b * data_set.batch_size + i
+ filename = mistake_filename_pattern.format(k, target[i])
+ torchvision.utils.save_image(img, filename)
+ print(Fore.RED + 'Wrote ' + filename + Style.RESET_ALL)
+ return ne
+
+######################################################################
+
+def train_model(model, model_filename, train_set, validation_set, nb_epochs_done = 0):
+ batch_size = args.batch_size
criterion = nn.CrossEntropyLoss()
if torch.cuda.is_available():
optimizer = optim.SGD(model.parameters(), lr = 1e-2)
- for k in range(0, args.nb_epochs):
+ start_t = time.time()
+
+ for e in range(nb_epochs_done, 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))
+ for b in range(0, train_set.nb_batches):
+ input, target = train_set.get_batch(b)
+ output = model.forward(Variable(input))
+ loss = criterion(output, Variable(target))
acc_loss = acc_loss + loss.data[0]
model.zero_grad()
loss.backward()
optimizer.step()
- log_string('train_loss {:d} {:f}'.format(k, acc_loss))
+ dt = (time.time() - start_t) / (e + 1)
- return model
+ log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss),
+ ' [ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + ']')
-######################################################################
+ torch.save([ model.state_dict(), e + 1 ], model_filename)
-def nb_errors(model, data_input, data_target):
- bs = args.batch_size
+ if validation_set is not None:
+ nb_validation_errors = nb_errors(model, validation_set)
- ne = 0
- 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)
+ 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, bs):
- if wta_prediction[i] != data_target.narrow(0, b, bs).data[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
######################################################################
for arg in vars(args):
log_string('argument ' + str(arg) + ' ' + str(getattr(args, arg)))
-for problem_number in range(1, 24):
- train_input, train_target = generate_set(problem_number,
- args.nb_train_batches * args.batch_size)
- test_input, test_target = generate_set(problem_number,
- args.nb_test_batches * args.batch_size)
- model = AfrozeShallowNet()
+######################################################################
+
+def int_to_suffix(n):
+ if n >= 1000000 and n%1000000 == 0:
+ return str(n//1000000) + 'M'
+ 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
+
+def save_exemplar_vignettes(data_set, nb, name):
+ n = torch.randperm(data_set.nb_samples).narrow(0, 0, nb)
+
+ for k in range(0, nb):
+ b = n[k] // data_set.batch_size
+ m = n[k] % data_set.batch_size
+ i, t = data_set.get_batch(b)
+ i = i[m].float()
+ i.sub_(i.min())
+ i.div_(i.max())
+ if k == 0: patchwork = Tensor(nb, 1, i.size(1), i.size(2))
+ patchwork[k].copy_(i)
+
+ torchvision.utils.save_image(patchwork, name)
- if torch.cuda.is_available():
- train_input, train_target = train_input.cuda(), train_target.cuda()
- test_input, test_target = test_input.cuda(), test_target.cuda()
- model.cuda()
+######################################################################
+
+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.compress_vignettes:
+ log_string('using_compressed_vignettes')
+ VignetteSet = svrtset.CompressedVignetteSet
+else:
+ log_string('using_uncompressed_vignettes')
+ VignetteSet = svrtset.VignetteSet
+
+########################################
+model_class = None
+for m in [ AfrozeShallowNet, AfrozeDeepNet, DeepNet2, DeepNet3 ]:
+ if args.model == m.name:
+ model_class = m
+ break
+if model_class is None:
+ print('Unknown model ' + args.model)
+ raise
+
+log_string('using model class ' + m.name)
+########################################
+
+for problem_number in map(int, args.problems.split(',')):
+
+ log_string('############### problem ' + str(problem_number) + ' ###############')
+
+ model = model_class()
- mu, std = train_input.data.mean(), train_input.data.std()
- train_input.data.sub_(mu).div_(std)
- test_input.data.sub_(mu).div_(std)
+ if torch.cuda.is_available(): model.cuda()
+
+ model_filename = model.name + '_pb:' + \
+ str(problem_number) + '_ns:' + \
+ int_to_suffix(args.nb_train_samples) + '.pth'
nb_parameters = 0
- for p in model.parameters():
- nb_parameters += p.numel()
+ for p in model.parameters(): nb_parameters += p.numel()
log_string('nb_parameters {:d}'.format(nb_parameters))
- model_filename = 'model_' + str(problem_number) + '.param'
+ ##################################################
+ # Tries to load the model
try:
- model.load_state_dict(torch.load(model_filename))
+ model_state_dict, nb_epochs_done = torch.load(model_filename)
+ model.load_state_dict(model_state_dict)
log_string('loaded_model ' + model_filename)
except:
- log_string('training_model')
- train_model(model, train_input, train_target)
- torch.save(model.state_dict(), model_filename)
+ nb_epochs_done = 0
+
+
+ ##################################################
+ # Train if necessary
+
+ if nb_epochs_done < args.nb_epochs:
+
+ log_string('training_model ' + model_filename)
+
+ t = time.time()
+
+ 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))
+ )
+
+ if args.nb_exemplar_vignettes > 0:
+ save_exemplar_vignettes(train_set, args.nb_exemplar_vignettes,
+ 'exemplar_{:d}.png'.format(problem_number))
+
+ 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, model_filename,
+ train_set, validation_set,
+ nb_epochs_done = nb_epochs_done)
+
log_string('saved_model ' + model_filename)
- nb_train_errors = nb_errors(model, train_input, train_target)
+ nb_train_errors = nb_errors(model, train_set)
+
+ log_string('train_error {:d} {:.02f}% {:d} {:d}'.format(
+ problem_number,
+ 100 * nb_train_errors / train_set.nb_samples,
+ nb_train_errors,
+ train_set.nb_samples)
+ )
+
+ ##################################################
+ # Test if necessary
+
+ if nb_epochs_done < args.nb_epochs or args.test_loaded_models:
+
+ t = time.time()
- 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))
- )
+ test_set = VignetteSet(problem_number,
+ args.nb_test_samples, args.batch_size,
+ cuda = torch.cuda.is_available())
- nb_test_errors = nb_errors(model, test_input, test_target)
+ nb_test_errors = nb_errors(model, test_set,
+ mistake_filename_pattern = 'mistake_{:06d}_{:d}.png')
- 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))
- )
+ log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(
+ problem_number,
+ 100 * nb_test_errors / test_set.nb_samples,
+ nb_test_errors,
+ test_set.nb_samples)
+ )
######################################################################
projects / pysvrt.git / blobdiff