Command Line Interface Reference

supercat

supercat [OPTIONS] COMMAND [ARGS]...

Options

-v, --version: Prints the current version.

--install-completion <install_completion>

Install completion for the specified shell.

Options: bash | zsh | fish | powershell | pwsh

--show-completion <show_completion>

Show completion for the specified shell, to copy it or customize the installation.

Options: bash | zsh | fish | powershell | pwsh

bibliography

supercat bibliography [OPTIONS]

bibtex

supercat bibtex [OPTIONS]

infer

supercat infer [OPTIONS]

Options

--gpu, --no-gpu

Whether or not to use a GPU for processing if available.

Default: True

--pretrained <pretrained>: The location (URL or filepath) of a pretrained model.

--reload, --no-reload

Should the pretrained model be downloaded again if it is online and already present locally.

Default: False

--dim <dim>

The dimension of the dataset. 2 or 3.

Default: 2

--items <items>

--item-dir <item_dir>: A directory with images to upscale.

--width <width>

The width of the final image/volume.

Default: 500

--height <height>: The height of the final image/volume.

--depth <depth>: The depth of the final image/volume.

--start-x <start_x>

--end-x <end_x>

--start-y <start_y>

--end-y <end_y>

--start-z <start_z>

--end-z <end_z>

--return-data, --no-return-data

Default: False

--output-dir <output_dir>: The location of the output directory. If not given then it uses the directory of the item.

--suffix <suffix>

The file extension for the output file.

Default

lr-finder

supercat lr-finder [OPTIONS]

Options

--plot-filename <plot_filename>

--start-lr <start_lr>

Default: 1e-07

--end-lr <end_lr>

Default: 10

--iterations <iterations>

Default: 100

--fp16, --no-fp16

Whether or not the floating-point precision of learner should be set to 16 bit.

Default: True

--output-dir <output_dir>

The location of the output directory.

Default: ./outputs

--weight-decay <weight_decay>: The amount of weight decay. If None then it uses the default amount of weight decay in fastai.

--dim <dim>

The dimension of the dataset. 2 or 3.

Default: 2

--deeprock <deeprock>: The path to the DeepRockSR dataset.

--downsample-scale <downsample_scale>

Should it use the 2x or 4x downsampled images.

Default: X4
Options: X2 | X4

--downsample-method <downsample_method>

Should it use the default method to downsample (bicubic) or a random kernel (UNKNOWN).

Default: unknown
Options: default | unknown

--batch-size <batch_size>

The batch size.

Default: 10

--force, --no-force

Whether or not to force the conversion of the bicubic upscaling.

Default: False

--max-samples <max_samples>: If set, then the number of input samples for training/validation is truncated at this number.

--include-sand, --no-include-sand

Including DeepSand-SR dataset.

Default: False

--pretrained <pretrained>

--initial-features <initial_features>: The number of features after the initial CNN layer. If not set then it is derived from the MACC.

--growth-factor <growth_factor>

The factor to grow the number of convolutional filters each time the model downscales.

Default: 2.0

--kernel-size <kernel_size>

The size of the kernel in the convolutional layers.

Default: 3

--stub-kernel-size <stub_kernel_size>

The size of the kernel in the initial stub convolutional layer.

Default: 7

--downblock-layers <downblock_layers>

The number of layers to downscale (and upscale) in the UNet.

Default: 4

--macc <macc>

The approximate number of multiply or accumulate operations in the model per pixel/voxel. Used to set initial_features if it is not provided explicitly.

Default: 132000

show-batch

supercat show-batch [OPTIONS]

Options

--output-path <output_path>: A location to save the HTML which summarizes the batch.

--dim <dim>

The dimension of the dataset. 2 or 3.

Default: 2

--deeprock <deeprock>: The path to the DeepRockSR dataset.

--downsample-scale <downsample_scale>

Should it use the 2x or 4x downsampled images.

Default: X4
Options: X2 | X4

--downsample-method <downsample_method>

Should it use the default method to downsample (bicubic) or a random kernel (UNKNOWN).

Default: unknown
Options: default | unknown

--batch-size <batch_size>

The batch size.

Default: 10

--force, --no-force

Whether or not to force the conversion of the bicubic upscaling.

Default: False

--max-samples <max_samples>: If set, then the number of input samples for training/validation is truncated at this number.

--include-sand, --no-include-sand

Including DeepSand-SR dataset.

Default: False

train

supercat train [OPTIONS]

Options

--distributed, --no-distributed

If the learner is distributed.

Default: False

--fp16, --no-fp16

Whether or not the floating-point precision of learner should be set to 16 bit.

Default: True

--output-dir <output_dir>

The location of the output directory.

Default: ./outputs

--weight-decay <weight_decay>: The amount of weight decay. If None then it uses the default amount of weight decay in fastai.

--dim <dim>

The dimension of the dataset. 2 or 3.

Default: 2

--deeprock <deeprock>: The path to the DeepRockSR dataset.

--downsample-scale <downsample_scale>

Should it use the 2x or 4x downsampled images.

Default: X4
Options: X2 | X4

--downsample-method <downsample_method>

Should it use the default method to downsample (bicubic) or a random kernel (UNKNOWN).

Default: unknown
Options: default | unknown

--batch-size <batch_size>

The batch size.

Default: 10

--force, --no-force

Whether or not to force the conversion of the bicubic upscaling.

Default: False

--max-samples <max_samples>: If set, then the number of input samples for training/validation is truncated at this number.

--include-sand, --no-include-sand

Including DeepSand-SR dataset.

Default: False

--pretrained <pretrained>

--initial-features <initial_features>: The number of features after the initial CNN layer. If not set then it is derived from the MACC.

--growth-factor <growth_factor>

The factor to grow the number of convolutional filters each time the model downscales.

Default: 2.0

--kernel-size <kernel_size>

The size of the kernel in the convolutional layers.

Default: 3

--stub-kernel-size <stub_kernel_size>

The size of the kernel in the initial stub convolutional layer.

Default: 7

--downblock-layers <downblock_layers>

The number of layers to downscale (and upscale) in the UNet.

Default: 4

--macc <macc>

The approximate number of multiply or accumulate operations in the model per pixel/voxel. Used to set initial_features if it is not provided explicitly.

Default: 132000

--epochs <epochs>

The number of epochs.

Default: 20

--freeze-epochs <freeze_epochs>

The number of epochs to train when the learner is frozen and the last layer is trained by itself. Only if fine_tune is set on the app.

Default: 3

--learning-rate <learning_rate>

The base learning rate (when fine tuning) or the max learning rate otherwise.

Default: 0.0001

--project-name <project_name>: The name for this project for logging purposes.

--run-name <run_name>: The name for this particular run for logging purposes.

--run-id <run_id>: A unique ID for this particular run for logging purposes.

--notes <notes>: A longer description of the run for logging purposes.

--tag <tag>: A tag for logging purposes. Multiple tags can be added each introduced with –tag.

--wandb, --no-wandb

Whether or not to use ‘Weights and Biases’ for logging.

Default: False

--wandb-mode <wandb_mode>

The mode for ‘Weights and Biases’.

Default: online

--wandb-dir <wandb_dir>: The location for ‘Weights and Biases’ output.

--wandb-entity <wandb_entity>: An entity is a username or team name where you’re sending runs.

--wandb-group <wandb_group>: Specify a group to organize individual runs into a larger experiment.

--wandb-job-type <wandb_job_type>: Specify the type of run, which is useful when you’re grouping runs together into larger experiments using group.

--mlflow, --no-mlflow

Whether or not to use MLflow for logging.

Default: False

tune

supercat tune [OPTIONS]

Options

--runs <runs>

The number of runs to attempt to train the model.

Default: 1

--engine <engine>

The optimizer to use to perform the hyperparameter tuning. Options: wandb, optuna, skopt.

Default: skopt

--id <id>

The ID of this hyperparameter tuning job. If using wandb, then this is the sweep id. If using optuna, then this is the storage. If using skopt, then this is the file to store the results.

Default

--name <name>

An informative name for this hyperparameter tuning job. If empty, then it creates a name from the project name.

Default

--method <method>

The sampling method to use to perform the hyperparameter tuning. By default it chooses the default method of the engine.

Default

--min-iter <min_iter>: The minimum number of iterations if using early termination. If left empty, then early termination is not used.

--seed <seed>: A seed for the random number generator.

--distributed, --no-distributed

If the learner is distributed.

Default: False

--fp16, --no-fp16

Whether or not the floating-point precision of learner should be set to 16 bit.

Default: True

--output-dir <output_dir>

The location of the output directory.

Default: ./outputs

--weight-decay <weight_decay>: The amount of weight decay. If None then it uses the default amount of weight decay in fastai.

--dim <dim>

The dimension of the dataset. 2 or 3.

Default: 2

--deeprock <deeprock>: The path to the DeepRockSR dataset.

--downsample-scale <downsample_scale>

Should it use the 2x or 4x downsampled images.

Default: X4
Options: X2 | X4

--downsample-method <downsample_method>

Should it use the default method to downsample (bicubic) or a random kernel (UNKNOWN).

Default: unknown
Options: default | unknown

--batch-size <batch_size>

The batch size.

Default: 10

--force, --no-force

Whether or not to force the conversion of the bicubic upscaling.

Default: False

--max-samples <max_samples>: If set, then the number of input samples for training/validation is truncated at this number.

--include-sand, --no-include-sand

Including DeepSand-SR dataset.

Default: False

--pretrained <pretrained>

--initial-features <initial_features>: The number of features after the initial CNN layer. If not set then it is derived from the MACC.

--growth-factor <growth_factor>: The factor to grow the number of convolutional filters each time the model downscales.

--kernel-size <kernel_size>: The size of the kernel in the convolutional layers.

--stub-kernel-size <stub_kernel_size>: The size of the kernel in the initial stub convolutional layer.

--downblock-layers <downblock_layers>: The number of layers to downscale (and upscale) in the UNet.

--macc <macc>

The approximate number of multiply or accumulate operations in the model per pixel/voxel. Used to set initial_features if it is not provided explicitly.

Default: 132000

--epochs <epochs>

The number of epochs.

Default: 20

--freeze-epochs <freeze_epochs>

The number of epochs to train when the learner is frozen and the last layer is trained by itself. Only if fine_tune is set on the app.

Default: 3

--learning-rate <learning_rate>

The base learning rate (when fine tuning) or the max learning rate otherwise.

Default: 0.0001

--project-name <project_name>: The name for this project for logging purposes.

--run-name <run_name>: The name for this particular run for logging purposes.

--run-id <run_id>: A unique ID for this particular run for logging purposes.

--notes <notes>: A longer description of the run for logging purposes.

--tag <tag>: A tag for logging purposes. Multiple tags can be added each introduced with –tag.

--wandb, --no-wandb

Whether or not to use ‘Weights and Biases’ for logging.

Default: False

--wandb-mode <wandb_mode>

The mode for ‘Weights and Biases’.

Default: online

--wandb-dir <wandb_dir>: The location for ‘Weights and Biases’ output.

--wandb-entity <wandb_entity>: An entity is a username or team name where you’re sending runs.

--wandb-group <wandb_group>: Specify a group to organize individual runs into a larger experiment.

--wandb-job-type <wandb_job_type>: Specify the type of run, which is useful when you’re grouping runs together into larger experiments using group.

--mlflow, --no-mlflow

Whether or not to use MLflow for logging.

Default: False

validate

supercat validate [OPTIONS]

Options

--gpu, --no-gpu

Whether or not to use a GPU for processing if available.

Default: True

--pretrained <pretrained>: The location (URL or filepath) of a pretrained model.

--reload, --no-reload

Should the pretrained model be downloaded again if it is online and already present locally.

Default: False

--dim <dim>

The dimension of the dataset. 2 or 3.

Default: 2

--deeprock <deeprock>: The path to the DeepRockSR dataset.

--downsample-scale <downsample_scale>

Should it use the 2x or 4x downsampled images.

Default: X4
Options: X2 | X4

--downsample-method <downsample_method>

Should it use the default method to downsample (bicubic) or a random kernel (UNKNOWN).

Default: unknown
Options: default | unknown

--batch-size <batch_size>

The batch size.

Default: 10

--force, --no-force

Whether or not to force the conversion of the bicubic upscaling.

Default: False

--max-samples <max_samples>: If set, then the number of input samples for training/validation is truncated at this number.

--include-sand, --no-include-sand

Including DeepSand-SR dataset.

Default: False