Args

Kagu parser

usage: kagu.arguments.base_arguments [-h] [--dataset DATASET] [--output OUTPUT] [--name NAME] [--p_name P_NAME]
                                     [--p_device {gpu,slurm,cpu,mps}] [--p_partition P_PARTITION] [--p_n_nodes P_N_NODES]
                                     [--p_n_gpus P_N_GPUS] [--p_n_cpus P_N_CPUS] [--p_ram P_RAM] [--p_backend {nccl,gloo}]
                                     [--p_verbose P_VERBOSE] [--p_logs P_LOGS] [--frame_size FRAME_SIZE [FRAME_SIZE ...]] [--snippet SNIPPET]
                                     [--step STEP] [--backbone {inception,resnet}] [--backbone_pretrained BACKBONE_PRETRAINED]
                                     [--backbone_frozen BACKBONE_FROZEN] [--predictor {lstm,gru}] [--dropout DROPOUT] [--teacher TEACHER]
                                     [--optimizer OPTIMIZER] [--lr LR] [--dbg] [--tb]

Named Arguments

--dataset

Path to dataset directory

Default: “data/kagu”

--output

Path to output directory. Will be created if does not exist

Default: “out”

--name

Name of the experiment for logging purposes

Default: “my_model”

--p_name

Platform job name for SLURM

Default: “job”

--p_device

Possible choices: gpu, slurm, cpu, mps

Platform device

Default: “gpu”

--p_partition

Platform partition for SLURM

Default: “general”

--p_n_nodes

Platform number of nodes for SLURM

Default: 1

--p_n_gpus

Platform number of GPUs per node

Default: 1

--p_n_cpus

Platform number of total CPUs per process/GPU

Default: 2

--p_ram

Platform total RAM in GB

Default: 10

--p_backend

Possible choices: nccl, gloo

Platform backend for IPC

Default: “nccl”

--p_verbose

Platform verbose

Default: True

--p_logs

Platform console logs path. Will be added to output folder automatically

Default: “./logs”

--frame_size

Frame size of images

Default: [299, 299]

--snippet

Number of frames to process

Default: 16

--step

The stride by which we process the frames. Same as snippet if not overlapping

Default: 8

--backbone

Possible choices: inception, resnet

Backbone encoder architecture

Default: “inception”

--backbone_pretrained

whether the backbone is pretrained or not

Default: True

--backbone_frozen

whether the backbone’s weights are frozen or not

Default: True

--predictor

Possible choices: lstm, gru

predictor unit

Default: “lstm”

--dropout

dropout rate, applied to hidden state(s)

Default: 0.4

--teacher

whether to apply teacher forcing

Default: True

--optimizer

Optimizer function to choose

Default: “adam”

--lr

Initial Learning Rate

Default: 1e-08

--dbg

Flag for debugging and development. Overrides log files.

Default: False

--tb

Flag for tb logging. If False, does not save tensorboard files.

Default: False