######################################################################
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()
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, 256, kernel_size=5, padding=2)
- self.conv3 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
- self.conv4 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
- self.conv5 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
- self.fc1 = nn.Linear(4096, 512)
+ 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)
x = fn.max_pool2d(x, kernel_size=2)
x = fn.relu(x)
- x = x.view(-1, 4096)
+ x = x.view(-1, 16 * self.nb_channels)
x = self.fc1(x)
x = fn.relu(x)
img = input[i].clone()
img.sub_(img.min())
img.div_(img.max())
- torchvision.utils.save_image(img,
- mistake_filename_pattern.format(b + i, target[i]))
-
+ 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
######################################################################
)
self.last_t = t
-def save_examplar_vignettes(data_set, nb, name):
+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):
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 = svrtset.CompressedVignetteSet
model_filename = model.name + '_pb:' + \
str(problem_number) + '_ns:' + \
- int_to_suffix(args.nb_train_samples) + '.state'
+ int_to_suffix(args.nb_train_samples) + '.pth'
nb_parameters = 0
for p in model.parameters(): nb_parameters += p.numel()
)
if args.nb_exemplar_vignettes > 0:
- save_examplar_vignettes(train_set, args.nb_exemplar_vignettes,
- 'examplar_{:d}.png'.format(problem_number))
+ 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,
else:
validation_set = None
- train_model(model, model_filename, train_set, validation_set, nb_epochs_done = nb_epochs_done)
+ 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_set)
cuda = torch.cuda.is_available())
nb_test_errors = nb_errors(model, test_set,
- mistake_filename_pattern = 'mistake_{:d}_{:06d}.png')
+ mistake_filename_pattern = 'mistake_{:06d}_{:d}.png')
log_string('test_error {:d} {:.02f}% {:d} {:d}'.format(
problem_number,