import time
import argparse
+import math
from colorama import Fore, Back, Style
+# Pytorch
+
import torch
from torch import optim
from torch.nn import functional as fn
from torchvision import datasets, transforms, utils
-import svrt
+# SVRT
+
+from vignette_set import VignetteSet, CompressedVignetteSet
######################################################################
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)
parser.add_argument('--log_file',
- type = str, default = 'cnn-svrt.log',
- help = 'Log file name')
+ type = str, default = 'default.log')
+
+parser.add_argument('--compress_vignettes',
+ action='store_true', default = True,
+ help = 'Use lossless compression to reduce the memory footprint')
+
+parser.add_argument('--deep_model',
+ action='store_true', default = True,
+ help = 'Use Afroze\'s Alexnet-like deep model')
+
+parser.add_argument('--test_loaded_models',
+ action='store_true', default = False,
+ help = 'Should we compute the test errors of loaded models')
args = parser.parse_args()
######################################################################
log_file = open(args.log_file, 'w')
+pred_log_t = None
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)
-
-######################################################################
+# Log and prints the string, with a time stamp. Does not log the
+# remark
+def log_string(s, remark = ''):
+ global pred_log_t
-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)
+
+ if pred_log_t is None:
+ elapsed = 'start'
+ else:
+ elapsed = '+{:.02f}s'.format(t - pred_log_t)
+
+ pred_log_t = t
+
+ log_file.write('[' + time.ctime() + '] ' + elapsed + ' ' + s + '\n')
+ log_file.flush()
+
+ print(Fore.BLUE + '[' + time.ctime() + '] ' + Fore.GREEN + elapsed + Style.RESET_ALL + ' ' + s + Fore.CYAN + remark + Style.RESET_ALL)
######################################################################
# -- full(120x84) -> 84 1
# -- full(84x2) -> 2 1
-class Net(nn.Module):
+class AfrozeShallowNet(nn.Module):
def __init__(self):
- super(Net, self).__init__()
+ super(AfrozeShallowNet, self).__init__()
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)
+ self.name = 'shallownet'
def forward(self, x):
x = fn.relu(fn.max_pool2d(self.conv1(x), kernel_size=2))
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))
+# 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__()
+ 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)
+ self.name = 'deepnet'
+
+ 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
+
+######################################################################
+
+def train_model(model, train_set):
+ batch_size = args.batch_size
+ criterion = nn.CrossEntropyLoss()
if torch.cuda.is_available():
- model.cuda()
criterion.cuda()
- optimizer, bs = optim.SGD(model.parameters(), lr = 1e-2), 100
+ optimizer = optim.SGD(model.parameters(), lr = 1e-2)
+
+ start_t = time.time()
- for k in range(0, args.nb_epochs):
+ for e 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))
+ 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)
+ log_string('train_loss {:d} {:f}'.format(e + 1, acc_loss),
+ ' [ETA ' + time.ctime(time.time() + dt * (args.nb_epochs - e)) + ']')
return model
######################################################################
-def nb_errors(model, data_input, data_target, bs = 100):
+def nb_errors(model, data_set):
ne = 0
-
- for b in range(0, data_input.size(0), bs):
- output = model.forward(data_input.narrow(0, b, bs))
+ 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, bs):
- if wta_prediction[i] != data_target.narrow(0, b, bs).data[i]:
+ for i in range(0, data_set.batch_size):
+ if wta_prediction[i] != target[i]:
ne = ne + 1
return ne
######################################################################
for arg in vars(args):
- log_string('ARGUMENT ' + str(arg) + ' ' + str(getattr(args, arg)))
+ 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_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()
- test_input, test_target = test_input.cuda(), test_target.cuda()
+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)
- mu, std = train_input.data.mean(), train_input.data.std()
- train_input.data.sub_(mu).div_(std)
- test_input.data.sub_(mu).div_(std)
+######################################################################
- model = train_model(train_input, train_target)
+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
- nb_train_errors = nb_errors(model, train_input, train_target)
+for problem_number in range(1, 24):
+
+ log_string('**** problem ' + str(problem_number) + ' ****')
+
+ if args.deep_model:
+ model = AfrozeDeepNet()
+ else:
+ model = AfrozeShallowNet()
- 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))
- )
+ if torch.cuda.is_available():
+ model.cuda()
- nb_test_errors = nb_errors(model, test_input, test_target)
+ model_filename = model.name + '_' + \
+ str(problem_number) + '_' + \
+ int_to_suffix(args.nb_train_samples) + '.param'
- 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))
- )
+ nb_parameters = 0
+ for p in model.parameters(): nb_parameters += p.numel()
+ log_string('nb_parameters {:d}'.format(nb_parameters))
+
+ need_to_train = False
+ try:
+ model.load_state_dict(torch.load(model_filename))
+ log_string('loaded_model ' + model_filename)
+ except:
+ need_to_train = True
+
+ 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())
+
+ log_string('data_generation {:0.2f} samples / s'.format(
+ train_set.nb_samples / (time.time() - t))
+ )
+
+ train_model(model, train_set)
+ torch.save(model.state_dict(), model_filename)
+ log_string('saved_model ' + model_filename)
+
+ 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)
+ )
+
+ 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())
+
+ 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(
+ problem_number,
+ 100 * nb_test_errors / test_set.nb_samples,
+ nb_test_errors,
+ test_set.nb_samples)
+ )
######################################################################
projects / pysvrt.git / blobdiff