dlk.core.losses package

Submodules

dlk.core.losses.bce module

class dlk.core.losses.bce.BCEWithLogitsLoss(config: dlk.core.losses.bce.BCEWithLogitsLossConfig)

Bases: object

binary crossentropy for bi-class classification

calc(result, inputs, rt_config)

calc the loss the predict is from result, the ground truth is from inputs

Parameters

• result – the model predict dict

• inputs – the all inputs for model

• rt_config – provide the current training status >>> { >>> “current_step”: self.global_step, >>> “current_epoch”: self.current_epoch, >>> “total_steps”: self.num_training_steps, >>> “total_epochs”: self.num_training_epochs >>> }

Returns

loss

update_config(rt_config: Dict)

callback for imodel to update the total steps and epochs

when init the loss module, the total step and epoch is not known, when all data ready, the imodel update the value for loss module

Parameters

rt_config – { “total_steps”: self.num_training_steps, “total_epochs”: self.num_training_epochs}

Returns

None

class dlk.core.losses.bce.BCEWithLogitsLossConfig(config: Dict)

Bases: dlk.core.base_module.BaseModuleConfig

Config for BCEWithLogitsLoss

Config Example:

>>> {
>>>     "config": {
>>>         "pred_truth_pair": [], # len(.) == 2, the 1st is the pred_name, 2nd is truth_name in __call__ inputs
>>>         "schedule": [1],
>>>         "masked_select": null, // if provide, only select the masked(=1) data
>>>         "scale": [1], # scale the loss for every schedule stage
>>>         // "schdeule": [0.3, 1.0], # can be a list or str
>>>         // "scale": "[0.5, 1]",
>>>     },
>>>     "_name": "bce",
>>> }

dlk.core.losses.cross_entropy module

class dlk.core.losses.cross_entropy.CrossEntropyLoss(config: dlk.core.losses.cross_entropy.CrossEntropyLossConfig)

Bases: object

for multi class classification

calc(result, inputs, rt_config)

calc the loss the predict is from result, the ground truth is from inputs

Parameters

• result – the model predict dict

• inputs – the all inputs for model

• rt_config – provide the current training status >>> { >>> “current_step”: self.global_step, >>> “current_epoch”: self.current_epoch, >>> “total_steps”: self.num_training_steps, >>> “total_epochs”: self.num_training_epochs >>> }

Returns

loss

update_config(rt_config)

callback for imodel to update the total steps and epochs

when init the loss module, the total step and epoch is not known, when all data ready, the imodel update the value for loss module

Parameters

rt_config – { “total_steps”: self.num_training_steps, “total_epochs”: self.num_training_epochs}

Returns

None

class dlk.core.losses.cross_entropy.CrossEntropyLossConfig(config: Dict)

Bases: dlk.core.base_module.BaseModuleConfig

Config for CrossEntropyLoss

Config Example:

>>> {
>>>     "config": {
>>>         "ignore_index": -1,
>>>         "weight": null, # or a list of value for every class
>>>         "label_smoothing": 0.0, # torch>=1.10
>>>         "pred_truth_pair": [], # len(.) == 2, the 1st is the pred_name, 2nd is truth_name in __call__ inputs
>>>         "schedule": [1],
>>>         "scale": [1], # scale the loss for every schedule stage
>>>         // "schdeule": [0.3, 1.0], # can be a list or str
>>>         // "scale": "[0.5, 1]",
>>>     },
>>>     "_name": "cross_entropy",
>>> }

dlk.core.losses.identity module

class dlk.core.losses.identity.IdentityLoss(config: dlk.core.losses.identity.IdentityLossConfig)

Bases: object

gather the loss and return when the loss is calc previor module like crf

calc(result, inputs, rt_config)

calc the loss the predict is from result, the ground truth is from inputs

Parameters

• result – the model predict dict

• inputs – the all inputs for model

• rt_config – provide the current training status >>> { >>> “current_step”: self.global_step, >>> “current_epoch”: self.current_epoch, >>> “total_steps”: self.num_training_steps, >>> “total_epochs”: self.num_training_epochs >>> }

Returns

loss

update_config(rt_config)

callback for imodel to update the total steps and epochs

when init the loss module, the total step and epoch is not known, when all data ready, the imodel update the value for loss module

Parameters

rt_config – { “total_steps”: self.num_training_steps, “total_epochs”: self.num_training_epochs}

Returns

None

class dlk.core.losses.identity.IdentityLossConfig(config: Dict)

Bases: dlk.core.base_module.BaseModuleConfig

Config for IdentityLoss

Config Example:

>>> {
>>>     config: {
>>>         "schedule": [1],
>>>         "scale": [1], # scale the loss for every schedule
>>>         // "schedule": [0.3, 1.0], # can be a list or str
>>>         // "scale": "[0.5, 1]",
>>>         "loss": "loss", // the real loss from result['loss']
>>>     },
>>>     _name: "identity",
>>> }

dlk.core.losses.mse module

class dlk.core.losses.mse.MSELoss(config: dlk.core.losses.mse.MSELossConfig)

Bases: object

mse loss for regression, distill, etc.

calc(result, inputs, rt_config)

calc the loss the predict is from result, the ground truth is from inputs

Parameters

• result – the model predict dict

• inputs – the all inputs for model

• rt_config – provide the current training status >>> { >>> “current_step”: self.global_step, >>> “current_epoch”: self.current_epoch, >>> “total_steps”: self.num_training_steps, >>> “total_epochs”: self.num_training_epochs >>> }

Returns

loss

update_config(rt_config)

callback for imodel to update the total steps and epochs

when init the loss module, the total step and epoch is not known, when all data ready, the imodel update the value for loss module

Parameters

rt_config – { “total_steps”: self.num_training_steps, “total_epochs”: self.num_training_epochs}

Returns

None

class dlk.core.losses.mse.MSELossConfig(config: Dict)

Bases: dlk.core.base_module.BaseModuleConfig

Config for MSELoss

Config Example:

>>> {
>>>     "config": {
>>>         "pred_truth_pair": [], # len(.) == 2, the 1st is the pred_name, 2nd is truth_name in __call__ inputs
>>>         "schedule": [1],
>>>         "masked_select": null, // if provide, only select the masked(=1) data
>>>         "scale": [1], # scale the loss for every schedule stage
>>>         // "schdeule": [0.3, 1.0], # can be a list or str
>>>         // "scale": "[0.5, 1]",
>>>     },
>>>     "_name": "mse",
>>> }

dlk.core.losses.multi_loss module

class dlk.core.losses.multi_loss.MultiLoss(config: dlk.core.losses.multi_loss.MultiLossConfig)

Bases: object

This module is NotImplemented yet don’t use it

calc(result, inputs, rt_config)

calc the loss the predict is from result, the ground truth is from inputs

Parameters

• result – the model predict dict

• inputs – the all inputs for model

• rt_config – provide the current training status >>> { >>> “current_step”: self.global_step, >>> “current_epoch”: self.current_epoch, >>> “total_steps”: self.num_training_steps, >>> “total_epochs”: self.num_training_epochs >>> }

Returns

loss

get_loss(config)

Use config to init the loss

Parameters

config – loss config

Returns

the Loss and the LossConfig

class dlk.core.losses.multi_loss.MultiLossConfig(config: Dict)

Bases: object

Config for MultiLoss

Config Example:

>>> {
>>>     "loss@the_first": {
>>>         config: {
>>>             "ignore_index": -1,
>>>             "weight": null, # or a list of value for every class
>>>             "label_smoothing": 0.0, # torch>=1.10
>>>             "pred_truth_pair": ["logits1", "label1"], # len(.) == 2, the 1st is the pred_name, 2nd is truth_name in __call__ inputs
>>>             "schedule": [0.3, 0.6, 1],
>>>             "scale": [1, 0, 0.5], # scale the loss for every schedule
>>>             // "schdeule": [0.3, 1.0],
>>>             // "scale": [0, 1, 0.5], # scale the loss
>>>         },
>>>         _name: "cross_entropy",
>>>     },
>>>     "loss@the_second": {
>>>         config: {
>>>             "pred_truth_pair": ["logits2", "label2"], # len(.) == 2, the 1st is the pred_name, 2nd is truth_name in __call__ inputs
>>>             "schdeule": [0.3, 0.6, 1],
>>>             "scale": [0, 1, 0.5], # scale the loss for every schedule
>>>             // "schdeule": [0.3, 1.0],
>>>             // "scale": [0, 1, 0.5], # scale the loss
>>>         },
>>>         _base: "cross_entropy",  // _name or _base is all ok
>>>     },
>>>     config: {
>>>         "loss_list": ['the_first', 'the_second'],
>>>     },
>>>     _name: "cross_entropy",
>>> }

Module contents

losses

dlk.core.losses.import_losses(losses_dir, namespace)