code stringlengths 17 6.64M |
|---|
def create_lr_scheduler(lr_scheduler_config, optimizer):
gamma = lr_scheduler_config['gamma']
if (lr_scheduler_config['type'] == 'multistep_lr'):
lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=lr_scheduler_config['milestones'], gamma=gamma, verbose=True)
else:
ra... |
def create_optimizer(optimizer_config, model):
lr = optimizer_config['lr']
weight_decay = optimizer_config['weight_decay']
momentum = optimizer_config['momentum']
if (optimizer_config['type'] == 'RMSProp'):
optimizer = torch.optim.RMSprop(model.parameters(), lr=lr, momentum=momentum, weight_de... |
class Benchmark():
def __init__(self, config_file):
self.config_file_path = config_file
self.config_file = read_yaml(config_file)
validate_config(self.config_file, 'benchmark', defaults=True)
torch.manual_seed(self.config_file['experiment']['seed'])
random.seed(self.config... |
def __main__():
parser = argparse.ArgumentParser(description='BioTorch')
parser.add_argument('--config_file', help='Path to the configuration file')
try:
args = parser.parse_args()
benchmark = Benchmark(args.config_file)
if (benchmark.benchmark_mode == 'training'):
benc... |
class CIFAR100(Dataset):
def __str__(self):
return 'CIFAR-100 Dataset'
def __init__(self, target_size, dataset_path='./datasets/cifar100', train_transforms=None, test_transforms=None):
self.mean = (0.5071, 0.4867, 0.4408)
self.std = (0.2675, 0.2565, 0.2761)
self.num_classes =... |
class CIFAR10(Dataset):
def __str__(self):
return 'CIFAR-10 Dataset'
def __init__(self, target_size, dataset_path='./datasets/cifar10', train_transforms=None, test_transforms=None):
self.mean = (0.4914, 0.4821, 0.4465)
self.std = (0.247, 0.2435, 0.2616)
self.num_classes = 10
... |
class CIFAR10Benchmark(Dataset):
def __str__(self):
return 'CIFAR-10 Benchmark Dataset'
def __init__(self, target_size, dataset_path='./datasets/cifar10', train_transforms=None, test_transforms=None):
self.mean = (0.4914, 0.4821, 0.4465)
self.std = (0.247, 0.2435, 0.2616)
sel... |
class Dataset(object):
def __init__(self, target_size, dataset_path, mean=None, std=None, train_transforms=None, test_transforms=None):
self.dataset_path = dataset_path
self.target_size = target_size
self.mean = mean
self.std = std
self.train_transforms = train_transforms
... |
class FashionMNIST(Dataset):
def __str__(self):
return 'Fashion MNIST Dataset'
def __init__(self, target_size, dataset_path='./datasets/fashion-mnist', train_transforms=None, test_transforms=None):
self.mean = (0.2859,)
self.std = (0.353,)
self.num_classes = 10
super(... |
class ImageNet(Dataset):
def __str__(self):
return 'Imagenet Dataset'
def __init__(self, target_size, dataset_path='./datasets/imagenet', train_transforms=None, test_transforms=None):
self.mean = (0.485, 0.456, 0.406)
self.std = (0.229, 0.224, 0.225)
self.num_classes = 1000
... |
class MNIST(Dataset):
def __str__(self):
return 'MNIST Dataset'
def __init__(self, target_size, dataset_path='./datasets/mnist', train_transforms=None, test_transforms=None):
self.mean = (0.1307,)
self.std = (0.3081,)
self.num_classes = 10
super(MNIST, self).__init__(... |
class DatasetSelector():
def __init__(self, dataset_name):
if (dataset_name not in DATASETS_AVAILABLE):
raise ValueError('Dataset name specified: {} not in the list of available datasets {}'.format(dataset_name, DATASETS_AVAILABLE))
self.dataset_name = dataset_name
def get_datase... |
class Evaluator():
def __init__(self, model, mode, loss_function, dataloader, device, output_dir, multi_gpu=False):
self.model = model
self.mode = mode
self.output_dir = output_dir
self.logs_dir = os.path.join(output_dir, 'logs')
self.loss_function = loss_function
... |
class Conv2d(nn.Conv2d):
def __init__(self, in_channels: int, out_channels: int, kernel_size: _size_2_t, stride: _size_2_t=1, padding: Union[(str, _size_2_t)]=0, dilation: _size_2_t=1, groups: int=1, bias: bool=True, padding_mode: str='zeros', layer_config: dict=None):
super(Conv2d, self).__init__(in_cha... |
class Linear(nn.Linear):
def __init__(self, in_features: int, out_features: int, bias: bool=True, layer_config: dict=None) -> None:
super(Linear, self).__init__(in_features, out_features, bias)
self.layer_config = layer_config
if (self.layer_config is None):
self.layer_config ... |
class Conv2d(fa_constructor.Conv2d):
'\n Implements the method from How Important Is Weight Symmetry in Backpropagation?\n with the modification of taking the absolute value of the Backward Matrix\n\n Batchwise Random Magnitude Sign-concordant Feedbacks (brSF):\n weight_backward = |M| ◦ sign(weight), ... |
class Linear(fa_constructor.Linear):
'\n Implements the method from How Important Is Weight Symmetry in Backpropagation?\n with the modification of taking the absolute value of the Backward Matrix\n\n Batchwise Random Magnitude Sign-concordant Feedbacks (brSF):\n weight_backward = |M| ◦ sign(weight), ... |
class Conv2d(nn.Conv2d):
def __init__(self, in_channels: int, out_channels: int, output_dim: int, kernel_size: _size_2_t, stride: _size_2_t=1, padding: Union[(str, _size_2_t)]=0, dilation: _size_2_t=1, groups: int=1, bias: bool=True, padding_mode: str='zeros', layer_config: dict=None):
super(Conv2d, self... |
class Linear(nn.Linear):
def __init__(self, in_features: int, out_features: int, output_dim: int, bias: bool=True, layer_config: dict=None) -> None:
super(Linear, self).__init__(in_features, out_features, bias)
self.layer_config = layer_config
if ('options' not in self.layer_config):
... |
class Conv2d(fa_constructor.Conv2d):
def __init__(self, in_channels: int, out_channels: int, kernel_size: _size_2_t, stride: _size_2_t=1, padding: Union[(str, _size_2_t)]=0, dilation: _size_2_t=1, groups: int=1, bias: bool=True, padding_mode: str='zeros', layer_config: dict=None):
if (layer_config is Non... |
class Linear(fa_constructor.Linear):
def __init__(self, in_features: int, out_features: int, bias: bool=True, layer_config: dict=None) -> None:
if (layer_config is None):
layer_config = {}
layer_config['type'] = 'fa'
super(Linear, self).__init__(in_features, out_features, bias... |
class Conv2d(nn.Conv2d):
def __init__(self, in_channels: int, out_channels: int, kernel_size: _size_2_t, stride: _size_2_t=1, padding: Union[(str, _size_2_t)]=0, dilation: _size_2_t=1, groups: int=1, bias: bool=True, padding_mode: str='zeros', layer_config: dict=None):
super(Conv2d, self).__init__(in_cha... |
class Linear(nn.Linear):
def __init__(self, in_features: int, out_features: int, bias: bool=True, layer_config: dict=None) -> None:
super(Linear, self).__init__(in_features, out_features, bias)
self.layer_config = layer_config
if (self.layer_config is None):
self.layer_config ... |
class Conv2d(fa_constructor.Conv2d):
'\n Implements the method from How Important Is Weight Symmetry in Backpropagation?\n with the modification of taking the absolute value of the Backward Matrix\n\n Fixed Random Magnitude Sign-concordant Feedbacks (frSF):\n weight_backward = |M| ◦ sign(weight), wher... |
class Linear(fa_constructor.Linear):
'\n Implements the method from How Important Is Weight Symmetry in Backpropagation?\n with the modification of taking the absolute value of the Backward Matrix\n\n Fixed Random Magnitude Sign-concordant Feedbacks (frSF):\n weight_backward = |M| ◦ sign(weight), wher... |
def compute_matrix_angle(A, B):
with torch.no_grad():
flat_A = torch.reshape(A, ((- 1),))
normalized_flat_A = (flat_A / torch.norm(flat_A))
flat_B = torch.reshape(B, ((- 1),))
normalized_flat_B = (flat_B / torch.norm(flat_B))
angle = ((180.0 / math.pi) * torch.arccos(torch.... |
class Conv2d(fa_constructor.Conv2d):
'\n Implements the method from How Important Is Weight Symmetry in Backpropagation?\n\n Uniform Sign-concordant Feedbacks (uSF):\n Backward Weights = sign(W)\n\n (https://arxiv.org/pdf/1510.05067.pdf)\n '
def __init__(self, in_channels: int, out_channels: i... |
class Linear(fa_constructor.Linear):
'\n Method from [How Important Is Weight Symmetry in Backpropagation?](https://arxiv.org/pdf/1510.05067.pdf)\n\n Uniform Sign-concordant Feedbacks (uSF):\n weight_backward = sign(weight)\n\n '
def __init__(self, in_features: int, out_features: int, bias: bool=... |
def convert_layer(layer, mode, copy_weights, layer_config=None, output_dim=None):
(layer_bias, bias_weight) = (False, None)
if (('weight' in layer.__dict__['_parameters']) and copy_weights):
weight = layer.weight
if (('bias' in layer.__dict__['_parameters']) and (layer.bias is not None)):
... |
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 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 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 ... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.