template.runner package

Subpackages

• template.runner.apply_model package
  • Submodules
  • template.runner.apply_model.apply_model module
  • template.runner.apply_model.evaluate module
  • template.runner.apply_model.setup module
  • Module contents
• template.runner.bidimensional package
  • Submodules
  • template.runner.bidimensional.bidimensional module
  • Module contents
• template.runner.image_classification package
  • Submodules
  • template.runner.image_classification.evaluate module
  • template.runner.image_classification.image_classification module
  • template.runner.image_classification.train module
  • Module contents
• template.runner.multi_label_image_classification package
  • Submodules
  • template.runner.multi_label_image_classification.evaluate module
  • template.runner.multi_label_image_classification.multi_label_image_classification module
  • template.runner.multi_label_image_classification.setup module
  • template.runner.multi_label_image_classification.train module
  • Module contents
• template.runner.process_activation package
  • Submodules
  • template.runner.process_activation.activation module
  • template.runner.process_activation.evaluate module
  • template.runner.process_activation.process_activation module
  • template.runner.process_activation.train module
  • Module contents
• template.runner.triplet package
  • Submodules
  • template.runner.triplet.evaluate module
  • template.runner.triplet.setup module
  • template.runner.triplet.train module
  • template.runner.triplet.transforms module
  • template.runner.triplet.triplet module
  • Module contents

Module contents

class template.runner.ApplyModel

Bases: object

static single_run(writer, current_log_folder, model_name, lr, output_channels, classify, **kwargs)

This is the main routine where train(), validate() and test() are called.

Parameters

• writer (Tensorboard SummaryWriter) – Responsible for writing logs in Tensorboard compatible format.

• current_log_folder (string) – Path to where logs/checkpoints are saved

• model_name (string) – Name of the model

• lr (float) – Value for learning rate

• kwargs (dict) – Any additional arguments.

• output_channels (int) – Specify shape of final layer of network.

• classify (boolean) – Specifies whether to generate a classification report for the data or not.

Returns

None – None

Return type

None

class template.runner.Bidimensional

Bases: template.runner.image_classification.image_classification.ImageClassification

static single_run(writer, current_log_folder, model_name, epochs, lr, decay_lr, validation_interval, checkpoint_all_epochs, **kwargs)

This is the main routine where train(), validate() and test() are called.

Parameters

• writer (Tensorboard.SummaryWriter) – Responsible for writing logs in Tensorboard compatible format.

• current_log_folder (string) – Path to where logs/checkpoints are saved

• model_name (string) – Name of the model

• epochs (int) – Number of epochs to train

• lr (float) – Value for learning rate

• kwargs (dict) – Any additional arguments.

• decay_lr (boolean) – Decay the lr flag

• validation_interval (int) – Run evaluation on validation set every N epochs

• checkpoint_all_epochs (bool) – If enabled, save checkpoint after every epoch.

Returns

• train_value (ndarray[floats] of size (1, epochs)) – Accuracy values for train split

• val_value (ndarray[floats] of size (1, `epochs`+1)) – Accuracy values for validation split

• test_value (float) – Accuracy value for test split

class template.runner.DivahisdbSemanticSegmentation

Bases: template.runner.image_classification.image_classification.ImageClassification

class_encoding = None

img_names_sizes_dict = None

classmethod prepare(model_name, **kwargs)

See parent class for documentation

class template.runner.ImageClassification

Bases: object

classmethod prepare(model_name, **kwargs)

Loads and prepares the data, the optimizer and the criterion

Parameters

• model_name (str) – Name of the model. Used for loading the model.

• kwargs (dict) – Any additional arguments.

Returns

• model (DataParallel) – The model to train

• num_classes (int) – How many different classes there are in our problem. Used for loading the model.

• best_value (float) – Best value of the model so far. Non-zero only in case of –resume being used

• train_loader (torch.utils.data.dataloader.DataLoader) – Training dataloader

• val_loader (torch.utils.data.dataloader.DataLoader) – Validation dataloader

• test_loader (torch.utils.data.dataloader.DataLoader) – Test set dataloader

• optimizer (torch.optim) – Optimizer to use during training, e.g. SGD

• criterion (torch.nn.modules.loss) – Loss function to use, e.g. cross-entropy

classmethod single_run(**kwargs)

This is the main routine where train(), validate() and test() are called.

Returns

• train_value (ndarray[floats] of size (1, epochs)) – Accuracy values for train split

• val_value (ndarray[floats] of size (1, `epochs`+1)) – Accuracy values for validation split

• test_value (float) – Accuracy value for test split

classmethod test_routine(model_name, num_classes, criterion, epochs, current_log_folder, writer, **kwargs)

Load the best model according to the validation score (early stopping) and runs the test routine.

Parameters

• model_name (str) – name of the model. Used for loading the model.

• num_classes (int) – How many different classes there are in our problem. Used for loading the model.

• criterion (torch.nn.modules.loss) – Loss function to use, e.g. cross-entropy

• epochs (int) – After how many epochs are we testing

• current_log_folder (string) – Path to where logs/checkpoints are saved

• writer (Tensorboard.SummaryWriter) – Responsible for writing logs in Tensorboard compatible format.

• kwargs (dict) – Any additional arguments.

Returns

test_value – Accuracy value for test split

Return type

float

classmethod train_routine(best_value, decay_lr, validation_interval, start_epoch, epochs, checkpoint_all_epochs, current_log_folder, **kwargs)

Performs the training and validatation routines

Parameters

• best_value (float) – Best value of the model so far. Non-zero only in case of –resume being used

• decay_lr (boolean) – Decay the lr flag

• validation_interval (int) – Run evaluation on validation set every N epochs

• start_epoch (int) – Int to initialize the starting epoch. Non-zero only in case of –resume being used

• epochs (int) – Number of epochs to train

• checkpoint_all_epochs (bool) – Save checkpoint at each epoch

• current_log_folder (string) – Path to where logs/checkpoints are saved

• kwargs (dict) – Any additional arguments.

Returns

• train_value (ndarray[floats] of size (1, epochs)) – Accuracy values for train split

• val_value (ndarray[floats] of size (1, `epochs`+1)) – Accuracy values for validation split

class template.runner.MultiLabelImageClassification

Bases: object

static single_run(writer, current_log_folder, model_name, epochs, lr, decay_lr, validation_interval, checkpoint_all_epochs, **kwargs)

This is the main routine where train(), validate() and test() are called.

Parameters

• writer (Tensorboard.SummaryWriter) – Responsible for writing logs in Tensorboard compatible format.

• current_log_folder (string) – Path to where logs/checkpoints are saved

• model_name (string) – Name of the model

• epochs (int) – Number of epochs to train

• lr (float) – Value for learning rate

• kwargs (dict) – Any additional arguments.

• decay_lr (boolean) – Decay the lr flag

• validation_interval (int) – Run evaluation on validation set every N epochs

• checkpoint_all_epochs (bool) – If enabled, save checkpoint after every epoch.

Returns

• train_value (ndarray[floats] of size (1, epochs)) – Accuracy values for train split

• val_value (ndarray[floats] of size (1, `epochs`+1)) – Accuracy values for validation split

• test_value (float) – Accuracy value for test split

class template.runner.ProcessActivation

Bases: object

static single_run(writer, current_log_folder, model_name, epochs, lr, decay_lr, validation_interval, checkpoint_all_epochs, **kwargs)

DESC

Parameters

Param – Desc

Returns

Return type

None

class template.runner.SemanticSegmentation

Bases: template.runner.image_classification.image_classification.ImageClassification

class_encoding = None

img_names_sizes_dict = None

classmethod prepare(model_name, **kwargs)

See parent class for documentation

class template.runner.Triplet

Bases: object

static single_run(writer, current_log_folder, model_name, epochs, lr, decay_lr, margin, anchor_swap, validation_interval, regenerate_every, checkpoint_all_epochs, only_evaluate, **kwargs)

This is the main routine where train(), validate() and test() are called.

Parameters

• writer (Tensorboard SummaryWriter) – Responsible for writing logs in Tensorboard compatible format.

• current_log_folder (string) – Path to where logs/checkpoints are saved

• model_name (string) – Name of the model

• epochs (int) – Number of epochs to train

• lr (float) – Value for learning rate

• margin (float) – The margin value for the triplet loss function

• anchor_swap (boolean) – Turns on anchor swap

• decay_lr (boolean) – Decay the lr flag

• validation_interval (int) – Run evaluation on validation set every N epochs

• regenerate_every (int) – Re-generate triplets every N epochs

• checkpoint_all_epochs (bool) – If enabled, save checkpoint after every epoch.

• only_evaluate (boolean) – Flag : if True, only the test set is loaded.

Returns

Mean Average Precision values for train and validation splits.

Return type

train_value, val_value, test_value