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def densenet121(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-121 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pr... |
def densenet161(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-161 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pr... |
def densenet169(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-169 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pr... |
def densenet201(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-201 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pr... |
def le_net_mnist(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output d... |
def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output d... |
def mnasnet0_5(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.5 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): I... |
def mnasnet0_75(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.75 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool):... |
def mnasnet1_0(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.0 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): I... |
def mnasnet1_3(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.3 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): I... |
def resnet18(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-18 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on... |
def resnet20(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-20 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on I... |
def resnet32(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-32 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on I... |
def resnet34(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-34 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on... |
def resnet44(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-44 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on I... |
def resnet50(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-50 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on... |
def resnet56(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-56 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on I... |
def resnet101(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-101 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained ... |
def resnet110(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-110 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on... |
def resnet152(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-152 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained ... |
def resnet1202(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-1202 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained ... |
def resnext50_32x4d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-50 32x4d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, r... |
def resnext101_32x8d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-101 32x8d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True,... |
def wide_resnet50_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-50-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n ... |
def wide_resnet101_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-101-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n ... |
class LeNetMNIST(nn.Module):
def __init__(self):
super(LeNetMNIST, self).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(in_channels=6, out_chan... |
def le_net_mnist(pretrained: bool=False, progress: bool=True, num_classes: int=10):
return LeNetMNIST()
|
class LeNetCIFAR(nn.Module):
def __init__(self):
super(LeNetCIFAR, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(in_channels=32, out_ch... |
def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10):
return LeNetCIFAR()
|
def alexnet(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> AlexNet:
'AlexNet model architecture from the\n `"One weird trick..." <https://arxiv.org/abs/1404.5997>`_ paper.\n The required minimum input size of the model is 63x63.\n Args:\n pretrained (bool... |
def densenet121(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-121 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pr... |
def densenet161(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-161 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pr... |
def densenet169(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-169 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pr... |
def densenet201(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> DenseNet:
'Densenet-201 model from\n `"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_.\n The required minimum input size of the model is 29x29.\n Args:\n pr... |
def le_net_mnist(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output d... |
def le_net_cifar(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config=None):
'\n Args:\n pretrained (bool): If True, returns a model pre-trained on ImageNet\n progress (bool): If True, displays a progress bar of the download to stderr\n num_classes (int): Output d... |
def mnasnet0_5(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.5 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): I... |
def mnasnet0_75(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 0.75 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool):... |
def mnasnet1_0(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.0 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): I... |
def mnasnet1_3(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config=None) -> MNASNet:
'MNASNet with depth multiplier of 1.3 from\n `"MnasNet: Platform-Aware Neural Architecture Search for Mobile"\n <https://arxiv.org/pdf/1807.11626.pdf>`_.\n Args:\n pretrained (bool): I... |
def resnet18(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-18 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on... |
def resnet20(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-20 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on I... |
def resnet32(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-32 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on I... |
def resnet34(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-34 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on... |
def resnet44(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-44 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on I... |
def resnet50(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-50 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on... |
def resnet56(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-56 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on I... |
def resnet101(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-101 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained ... |
def resnet110(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-110 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained on... |
def resnet152(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNet-152 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained ... |
def resnet1202(pretrained: bool=False, progress: bool=True, num_classes: int=10, layer_config: dict=None) -> ResNet:
'ResNet-1202 model from\n `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.\n\n Args:\n pretrained (bool): If True, returns a model pre-trained ... |
def resnext50_32x4d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-50 32x4d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True, r... |
def resnext101_32x8d(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'ResNeXt-101 32x8d model from\n `"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.\n\n Args:\n pretrained (bool): If True,... |
def wide_resnet50_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-50-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n ... |
def wide_resnet101_2(pretrained: bool=False, progress: bool=True, num_classes: int=1000, layer_config: dict=None) -> ResNet:
'Wide ResNet-101-2 model from\n `"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.\n\n The model is the same as ResNet except for the bottleneck number of channels\n ... |
def create_torchvision_biomodel(model_architecture, mode, layer_config: dict=None, pretrained: bool=False, progress: bool=True, num_classes: int=1000) -> BioModule:
if (not pretrained):
copy_weights = False
model = model_architecture(pretrained, progress, num_classes=num_classes)
else:
... |
def create_le_net_biomodel(model_architecture, mode, layer_config: dict=None, pretrained: bool=False, progress: bool=True, num_classes: int=10) -> BioModule:
model = model_architecture(pretrained, progress, num_classes=num_classes)
return BioModule(model, mode=mode, copy_weights=False, layer_config=layer_conf... |
def apply_xavier_init(module):
if (isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d)):
nn.init.xavier_uniform_(module.weight)
if (module.bias is not None):
nn.init.constant_(module.bias, 0)
|
class BioModule(nn.Module):
def __init__(self, module, mode='fa', copy_weights=True, layer_config=None, output_dim=None):
super(BioModule, self).__init__()
self.module = module
self.mode = mode
self.output_dim = output_dim
self.copy_weights = copy_weights
if (layer... |
class ModuleConverter():
def __init__(self, mode='fa'):
self.mode = mode
def convert(self, module, copy_weights=True, layer_config=None, output_dim=None):
layer_counts = self.count_layers(module)
self.replaced_layers_counts = defaultdict((lambda : 0))
self._replace_layers_rec... |
def train(model, mode, loss_function, optimizer, train_dataloader, device, epoch, multi_gpu, top_k=5, display_iterations=500):
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
topk = Ave... |
def test(model, loss_function, test_dataloader, device, top_k=5):
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('Acc@1', ':6.2f')
topk = AverageMeter(('Acc@' + str(top_k)), ':6.2f')
model.eval()
with torch.no_grad():
end = time.tim... |
def adjust_learning_rate(optimizer, epoch, args):
'Sets the learning rate to the initial LR decayed by 10 every 30 epochs'
lr = (args.lr * (0.1 ** (epoch // 30)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
|
def compute_angles_module(module):
queue = deque()
layers_alignment = OrderedDict()
seen_keys = defaultdict((lambda : 0))
for module_keys in module._modules.keys():
queue.append((module, module_keys))
while (len(queue) > 0):
(module, module_key) = queue.popleft()
layer = ge... |
def compute_weight_ratio_module(module, mode):
queue = deque()
weight_diff = OrderedDict()
seen_keys = defaultdict((lambda : 0))
for module_keys in module._modules.keys():
queue.append((module, module_keys))
while (len(queue) > 0):
(module, module_key) = queue.popleft()
lay... |
class AverageMeter(object):
'Computes and stores the average and current value'
def __init__(self, name, fmt=':f'):
self.name = name
self.fmt = fmt
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(... |
class ProgressMeter(object):
def __init__(self, num_batches, meters, prefix=''):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch):
entries = [(self.prefix + self.batch_fmtstr.format(batch))]
ent... |
def accuracy(output, target, topk=(1,)):
'Computes the accuracy over the k top predictions for the specified values of k'
with torch.no_grad():
maxk = max(topk)
batch_size = target.size(0)
(_, pred) = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(ta... |
class Trainer():
def __init__(self, model, mode, loss_function, optimizer, lr_scheduler, train_dataloader, val_dataloader, device, epochs, output_dir, metrics_config, multi_gpu=False):
self.model = model
self.mode = mode
self.output_dir = output_dir
self.logs_dir = os.path.join(ou... |
def read_yaml(yaml_path):
with open(yaml_path, 'r') as f:
yaml_file = yaml.load(f, Loader=yaml.Loader)
return yaml_file
|
def mkdir(path):
if (not os.path.exists(path)):
return os.makedirs(path)
|
def mkdirs(paths):
if (isinstance(paths, list) and (not isinstance(paths, str))):
for path in paths:
mkdir(path)
else:
mkdir(paths)
|
def path_exists(path):
if os.path.exists(path):
return True
else:
raise ValueError('Path provided does not exist.')
|
def read_schema(schema_name):
with open(os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'schemas', (schema_name + '.json')))) as schema:
return json.load(schema)
|
def validate_config(instance, schema_name, defaults=True):
with open(os.path.normpath(os.path.join(os.path.dirname(__file__), '..', 'schemas', (schema_name + '.json')))) as schema:
if defaults:
default_validator = extend_schema_with_default(Draft7Validator)
try:
def... |
def extend_schema_with_default(validator_class):
validate_properties = validator_class.VALIDATORS['properties']
def set_defaults(validator, properties, instance, schema):
for (property_, subschema) in properties.items():
if (('default' in subschema) and (not isinstance(instance, list))):
... |
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_si... |
def tflog2pandas(path: str) -> pd.DataFrame:
'convert single tensorflow log file to pandas DataFrame\n Parameters\n ----------\n path : str\n path to tensorflow log file\n Returns\n -------\n pd.DataFrame\n converted dataframe\n '
DEFAULT_SIZE_GUIDANCE = {'compressedHistogra... |
def sorting_function(x1, x2):
x1_s = x1.split('_')
x2_s = x2.split('_')
if (int(x1_s[1]) < int(x2_s[1])):
return (- 1)
elif (int(x1_s[1]) > int(x2_s[1])):
return 1
elif (x1_s[0] <= x2_s[0]):
return (- 1)
else:
return 1
|
def get_layer_alignment(dir_logs, net='resnet'):
layers_paths = [folder for folder in os.listdir(dir_logs)]
event_paths = []
layers_alignment = {}
for l_p in layers_paths:
if ('layer_alignment' in l_p):
log_path = glob.glob(os.path.join(dir_logs, l_p, 'event*'))
if (len... |
def get_layer_weights(dir_logs, net='resnet', normalization=None):
layers_paths = [folder for folder in os.listdir(dir_logs)]
event_paths = []
layers_alignment = {}
for l_p in layers_paths:
if ('weight_difference' in l_p):
log_path = glob.glob(os.path.join(dir_logs, l_p, 'event*'))... |
def mkdir(path):
if (not os.path.exists(path)):
return os.makedirs(path)
|
def plot_multiple_lists(ydata, xdata, x_axis_name, y_axis_name, title, save_dir, figname, cmap='winter'):
n = len(ydata)
cmap_ = plt.cm.get_cmap(cmap)
colors = iter(cmap_(np.linspace(0, 1, n)))
colors_cmap = cmap_(np.arange(cmap_.N))
Z = [[0, 0], [0, 0]]
levels = range(0, n, 1)
CS3 = plt.c... |
class FGSM(Attack):
"\n FGSM in the paper 'Explaining and harnessing adversarial examples'\n [https://arxiv.org/abs/1412.6572]\n Distance Measure : Linf\n Arguments:\n model (nn.Module): model to attack.\n eps (float): maximum perturbation. (Default: 0.007)\n Shape:\n - images:... |
class PGD(Attack):
"\n PGD in the paper 'Towards Deep Learning Models Resistant to Adversarial Attacks'\n [https://arxiv.org/abs/1706.06083]\n Distance Measure : Linf\n Arguments:\n model (nn.Module): model to attack.\n eps (float): maximum perturbation. (Default: 0.3)\n alpha (fl... |
class TPGD(Attack):
"\n PGD based on KL-Divergence loss in the paper 'Theoretically Principled Trade-off between Robustness and Accuracy'\n [https://arxiv.org/abs/1901.08573]\n Distance Measure : Linf\n Arguments:\n model (nn.Module): model to attack.\n eps (float): strength of the attac... |
@pytest.fixture(scope='session')
def config_bp_path():
return os.path.abspath(os.path.join('tests', 'fixtures', 'config_files', 'config_bp.yaml'))
|
@pytest.fixture(scope='session')
def config_usf_reproducible_path():
return os.path.abspath(os.path.join('tests', 'fixtures', 'config_files', 'config_usf_reproducible.yaml'))
|
def test_benchmark(config_bp_path):
benchmark = Benchmark(config_bp_path)
benchmark.run()
current_files = os.listdir('tests/tmp/mnist/le_net/backpropagation_test/')
expected_files = ['best_acc.txt', 'config.yaml', 'latest_model.pth', 'results.csv', 'results.json', 'model_best_acc.pth', 'logs']
for... |
def test_benchmark_command_line_reproducibility_cpu(config_usf_reproducible_path):
cmd = ['python', 'benchmark.py', '--config', config_usf_reproducible_path]
subprocess.run(cmd)
results_1 = pd.read_json('tests/tmp/mnist/le_net/usf_test/results.json')
cmd = ['python', 'benchmark.py', '--config', config... |
@pytest.fixture(scope='session')
def mode_types():
return ['backpropagation', 'fa', 'dfa', 'usf', 'brsf', 'frsf']
|
class Model(nn.Module):
def __init__(self):
super(Model, self).__init__()
self.conv1 = nn.Conv2d(1, 20, 3)
self.relu = nn.ReLU()
self.fc = nn.Linear(20, 10)
def forward(self, x):
out = self.relu(self.conv1(x))
out = F.avg_pool2d(out, out.size()[3])
ret... |
@pytest.fixture(scope='function')
def dummy_net():
return Model()
|
@pytest.fixture(scope='function')
def dummy_net_constructor():
return Model
|
@pytest.fixture(scope='session')
def datasets_available():
return ['mnist', 'cifar10', 'cifar10_benchmark', 'cifar100', 'fashion_mnist', 'imagenet']
|
def test_datasets_implemented(datasets_available):
for dataset_name in datasets_available:
assert DatasetSelector(dataset_name).get_dataset()
|
@pytest.fixture(scope='session')
def model_architectures():
return [('le_net_mnist', (1, 1, 32, 32)), ('le_net_cifar', (1, 3, 32, 32)), ('resnet18', (1, 3, 128, 128)), ('resnet20', (1, 3, 128, 128)), ('resnet56', (1, 3, 128, 128))]
|
def check_model(model, input_size):
model_ = model()
if (('mode' in model_.__dict__) and (model_.mode == 'dfa')):
_ = model_.forward(torch.rand(input_size), targets=torch.LongTensor([1]), loss_function=torch.nn.CrossEntropyLoss())
else:
_ = model_(torch.rand(input_size))
|
def test_backpropagation_models(model_architectures):
for (arch, input_size) in model_architectures:
check_model(models.backpropagation.__dict__[arch], input_size)
|
def test_fa_models(model_architectures):
for (arch, input_size) in model_architectures:
check_model(models.fa.__dict__[arch], input_size)
|
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