easycv.models.loss package

Submodules

easycv.models.loss.iou_loss module

class easycv.models.loss.iou_loss.IOUloss(reduction='none', loss_type='iou')

Bases: torch.nn.modules.module.Module

__init__(reduction='none', loss_type='iou')

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(pred, target)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

easycv.models.loss.mse_loss module

class easycv.models.loss.mse_loss.JointsMSELoss(use_target_weight=False, loss_weight=1.0)

Bases: torch.nn.modules.module.Module

MSE loss for heatmaps.

Parameters

• use_target_weight (bool) – Option to use weighted MSE loss. Different joint types may have different target weights.

• loss_weight (float) – Weight of the loss. Default: 1.0.

__init__(use_target_weight=False, loss_weight=1.0)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(output, target, target_weight)

Forward function.

training: bool

easycv.models.loss.pytorch_metric_learning module

class easycv.models.loss.pytorch_metric_learning.FocalLoss2d(gamma=2, weight=None, size_average=None, reduce=None, reduction='mean', num_classes=2)

Bases: torch.nn.modules.loss._WeightedLoss

__init__(gamma=2, weight=None, size_average=None, reduce=None, reduction='mean', num_classes=2)

FocalLoss2d, loss solve 2-class classification unbalance problem

Parameters

• gamma – focal loss param Gamma

• weight – weight same as loss._WeightedLoss

• size_average – size_average same as loss._WeightedLoss

• reduce – reduce same as loss._WeightedLoss

• reduction – reduce same as loss._WeightedLoss

• num_classes – fix num 2

Returns

Focalloss nn.module.loss object

forward(input, target)

input: [N * num_classes] target : [N * num_classes] one-hot

reduction: str

class easycv.models.loss.pytorch_metric_learning.DistributeMSELoss

Bases: torch.nn.modules.module.Module

__init__()

DistributeMSELoss : for faceid age, score predict (regression by softmax)

forward(input, target)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class easycv.models.loss.pytorch_metric_learning.CrossEntropyLossWithLabelSmooth(label_smooth=0.1, temperature=1.0, with_cls=False, embedding_size=512, num_classes=10000)

Bases: torch.nn.modules.module.Module

__init__(label_smooth=0.1, temperature=1.0, with_cls=False, embedding_size=512, num_classes=10000)

A softmax loss , with label_smooth and fc(to fit pytorch metric learning interface) :param label_smooth: label_smooth args, default=0.1 :param with_cls: if True, will generate a nn.Linear to trans input embedding from embedding_size to num_classes :param embedding_size: if input is feature not logits, then need this to indicate embedding shape :param num_classes: if input is feature not logits, then need this to indicate classification num_classes

Returns

None

Raises

IOError – An error occurred accessing the bigtable.Table object.

forward(input, target)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class easycv.models.loss.pytorch_metric_learning.AMSoftmaxLoss(embedding_size=512, num_classes=100000, margin=0.35, scale=30)

Bases: torch.nn.modules.module.Module

__init__(embedding_size=512, num_classes=100000, margin=0.35, scale=30)

AMsoftmax loss , with fc(to fit pytorch metric learning interface), paper: https://arxiv.org/pdf/1801.05599.pdf :param embedding_size: forward input [N, embedding_size ] :param num_classes: classification num_classes :param margin: AMSoftmax param :param scale: AMSoftmax param, should increase num_classes

forward(x, lb)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class easycv.models.loss.pytorch_metric_learning.ModelParallelSoftmaxLoss(embedding_size=512, num_classes=100000, scale=None, margin=None, bias=True)

Bases: torch.nn.modules.module.Module

__init__(embedding_size=512, num_classes=100000, scale=None, margin=None, bias=True)

ModelParallel Softmax by sailfish :param embedding_size: forward input [N, embedding_size ] :param num_classes: classification num_classes

forward(x, lb)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class easycv.models.loss.pytorch_metric_learning.ModelParallelAMSoftmaxLoss(embedding_size=512, num_classes=100000, margin=0.35, scale=30)

Bases: torch.nn.modules.module.Module

__init__(embedding_size=512, num_classes=100000, margin=0.35, scale=30)

ModelParallel AMSoftmax by sailfish :param embedding_size: forward input [N, embedding_size ] :param num_classes: classification num_classes

forward(x, lb)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool

class easycv.models.loss.pytorch_metric_learning.SoftTargetCrossEntropy(num_classes=1000, **kwargs)

Bases: torch.nn.modules.module.Module

__init__(num_classes=1000, **kwargs)

Initializes internal Module state, shared by both nn.Module and ScriptModule.

forward(x: torch.Tensor, target: torch.Tensor) → torch.Tensor

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

training: bool