mapreader.classify.classifier_context

Module Contents

Classes

ClassifierContextContainer

class mapreader.classify.classifier_context.ClassifierContextContainer(model, dataloaders, labels_map, device='default', input_size=(224, 224), is_inception=False, load_path=None, force_device=False, **kwargs)

Bases: mapreader.classify.classifier.ClassifierContainer

Parameters:

  • model (Union[str, torch.nn.Module, None]) –

  • dataloaders (Union[Dict[str, torch.utils.data.DataLoader], None]) –

  • labels_map (Union[Dict[int, str], None]) –

  • device (Optional[str]) –

  • input_size (Optional[int]) –

  • is_inception (Optional[bool]) –

  • load_path (Optional[str]) –

  • force_device (Optional[bool]) –

train(phases=['train', 'val'], num_epochs=25, save_model_dir='models', verbosity_level=1, tensorboard_path=None, tmp_file_save_freq=2, remove_after_load=True, print_info_batch_freq=5)

Train the model on the specified phases for a given number of epochs. Wrapper function for train_core method to capture exceptions (with supported exceptions so far: KeyboardInterrupt). Refer to train_core for more information.

Parameters:

  • phases (list of str, optional) – The phases to train the model on for each epoch. Default is ["train", "val"].

  • num_epochs (int, optional) – The number of epochs to train the model for. Default is 25.

  • save_model_dir (str or None, optional) – The directory to save the model in. Default is "models". If set to None, the model is not saved.

  • verbosity_level (int, optional) –

    The level of verbosity during training:

    • 0 is silent,

    • 1 is progress bar and metrics,

    • 2 is detailed information.

    Default is 1.

  • tensorboard_path (str or None, optional) – The path to the directory to save TensorBoard logs in. If set to None, no TensorBoard logs are saved. Default is None.

  • tmp_file_save_freq (int, optional) – The frequency (in epochs) to save a temporary file of the model. Default is 2. If set to 0 or None, no temporary file is saved.

  • remove_after_load (bool, optional) – Whether to remove the temporary file after loading it. Default is True.

  • print_info_batch_freq (int, optional) – The frequency (in batches) to print training information. Default is 5. If set to 0 or None, no training information is printed.

Returns:

The function saves the model to the save_model_dir directory, and optionally to a temporary file. If interrupted with a KeyboardInterrupt, the function tries to load the temporary file. If no temporary file is found, it continues without loading.

Return type:

None

train_core(phases=['train', 'val'], num_epochs=25, save_model_dir='models', verbosity_level=1, tensorboard_path=None, tmp_file_save_freq=2, print_info_batch_freq=5)

Trains/fine-tunes a classifier for the specified number of epochs on the given phases using the specified hyperparameters.

Parameters:

  • phases (list of str, optional) – The phases to train the model on for each epoch. Default is ["train", "val"].

  • num_epochs (int, optional) – The number of epochs to train the model for. Default is 25.

  • save_model_dir (str or None, optional) – The directory to save the model in. Default is "models". If set to None, the model is not saved.

  • verbosity_level (int, optional) –

    The level of verbosity during training:

    • 0 is silent,

    • 1 is progress bar and metrics,

    • 2 is detailed information.

    Default is 1.

  • tensorboard_path (str or None, optional) – The path to the directory to save TensorBoard logs in. If set to None, no TensorBoard logs are saved. Default is None.

  • tmp_file_save_freq (int, optional) – The frequency (in epochs) to save a temporary file of the model. Default is 2. If set to 0 or None, no temporary file is saved.

  • print_info_batch_freq (int, optional) – The frequency (in batches) to print training information. Default is 5. If set to 0 or None, no training information is printed.

Raises:

  • ValueError –

    If the criterion is not set. Use the add_criterion method to set the criterion.

    If the optimizer is not set and the phase is “train”. Use the initialize_optimizer or add_optimizer method to set the optimizer.

  • KeyError – If the specified phase cannot be found in the object’s dataloader with keys.

Return type:

None

show_sample(set_name='train', batch_number=1, print_batch_info=True, figsize=(15, 10))

Displays a sample of training or validation data in a grid format with their corresponding class labels.

Parameters:

  • set_name (str, optional) – Name of the dataset (train/validation) to display the sample from, by default "train".

  • batch_number (int, optional) – Number of batches to display, by default 1.

  • print_batch_info (bool, optional) – Whether to print information about the batch size, by default True.

  • figsize (tuple, optional) – Figure size (width, height) in inches, by default (15, 10).

Returns:

Displays the sample images with their corresponding class labels.

Return type:

None

Raises:

StopIteration – If the specified number of batches to display exceeds the total number of batches in the dataset.

Notes

This method uses the dataloader of the ImageClassifierData class and the torchvision.utils.make_grid function to display the sample data in a grid format. It also calls the _imshow method of the ImageClassifierData class to show the sample data.

generate_layerwise_lrs(min_lr, max_lr, spacing='linspace', sep_group_names=['features1', 'features2'])

Calculates layer-wise learning rates for a given set of model parameters.

Parameters:

  • min_lr (float) – The minimum learning rate to be used.

  • max_lr (float) – The maximum learning rate to be used.

  • spacing (str, optional) – The type of sequence to use for spacing the specified interval learning rates. Can be either "linspace" or "geomspace", where “linspace” uses evenly spaced learning rates over a specified interval and “geomspace” uses learning rates spaced evenly on a log scale (a geometric progression). By default "linspace".

  • sep_group_names (list, optional) – A list of strings containing the names of parameter groups. Layers belonging to each group will be assigned the same learning rate. Defaults to ["features1", "features2"].

Returns:

A list of dictionaries containing the parameters and learning rates for each layer.

Return type:

list of dicts

show_inference_sample_results(label, num_samples=6, set_name='train', min_conf=None, max_conf=None, figsize=(15, 15))

Shows a sample of the results of the inference.

Parameters:

  • label (str, optional) – The label for which to display results.

  • num_samples (int, optional) – The number of sample results to display. Defaults to 6.

  • set_name (str, optional) – The name of the dataset split to use for inference. Defaults to "train".

  • min_conf (float, optional) – The minimum confidence score for a sample result to be displayed. Samples with lower confidence scores will be skipped. Defaults to None.

  • max_conf (float, optional) – The maximum confidence score for a sample result to be displayed. Samples with higher confidence scores will be skipped. Defaults to None.

  • figsize (tuple[int, int], optional) – Figure size (width, height) in inches, displaying the sample results. Defaults to (15, 15).

Return type:

None