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argparser.py
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import argparse
import tasks
def modify_command_options(opts):
opts.pooling = round(opts.crop_size / opts.output_stride)
if opts.dataset == 'voc':
opts.num_classes = 21
if opts.dataset == 'ade':
opts.num_classes = 150
if not opts.visualize:
opts.sample_num = 0
if opts.method is not None:
if opts.method == 'FT':
pass
if opts.method == 'LWF':
opts.loss_kd = 100
# if opts.method == 'LWF-MC':
# opts.icarl = True
# opts.icarl_importance = 10
if opts.method == 'ILT':
opts.loss_kd = 100
opts.loss_de = 100
if opts.method.upper() == 'MIB':
opts.loss_kd = 10 if "15-1" not in opts.task else 100
opts.unce = True
opts.unkd = True
opts.init_balanced = True
if opts.method == 'PLOP':
if opts.overlap:
opts.threshold = 0.001
opts.pod_factor = 0.0005
else:
opts.threshold = 0.5
opts.pod_factor = 0.0001
opts.pod = True
opts.pseudo = True
opts.init_balanced = True
if opts.method == "IC":
opts.icarl = True
opts.icarl_bkg = -1
opts.no_overlap = not opts.overlap
return opts
def get_argparser():
parser = argparse.ArgumentParser()
# Performance Options
parser.add_argument("--local_rank", type=int, default=0)
parser.add_argument("--random_seed", type=int, default=42,
help="random seed (default: 42)")
parser.add_argument("--num_workers", type=int, default=1,
help='number of workers (default: 1)')
# Datset Options
parser.add_argument("--data_root", type=str, default='data',
help="path to Dataset")
parser.add_argument("--dataset", type=str, default='voc',
choices=['voc', 'ade'], help='Name of dataset')
parser.add_argument("--num_classes", type=int, default=None,
help="num classes (default: None)")
# Method Options
# BE CAREFUL USING THIS, THEY WILL OVERRIDE ALL THE OTHER PARAMETERS.
parser.add_argument("--method", type=str, default=None,
choices=['FT', 'LWF', 'IC', 'ILT', 'EWC', 'RW', 'PI', 'MiB', 'PLOP'],
help="The method you want to use. BE CAREFUL USING THIS, IT MAY OVERRIDE OTHER PARAMETERS.")
# Train Options
parser.add_argument("--epochs", type=int, default=30,
help="epoch number (default: 30)")
parser.add_argument("--batch_size", type=int, default=24,
help='batch size (default: 24)')
parser.add_argument("--crop_size", type=int, default=512,
help="crop size (default: 512)")
parser.add_argument("--lr", type=float, default=0.001,
help="learning rate (default: 0.001)")
parser.add_argument("--momentum", type=float, default=0.9,
help='momentum for SGD (default: 0.9)')
parser.add_argument("--weight_decay", type=float, default=1e-4,
help='weight decay (default: 1e-4)')
parser.add_argument("--lr_policy", type=str, default='poly',
choices=['poly', 'step'], help="lr schedule policy (default: poly)")
parser.add_argument("--lr_decay_step", type=int, default=5000,
help="decay step for stepLR (default: 5000)")
parser.add_argument("--lr_decay_factor", type=float, default=0.1,
help="decay factor for stepLR (default: 0.1)")
parser.add_argument("--lr_power", type=float, default=0.9,
help="power for polyLR (default: 0.9)")
parser.add_argument("--bce", default=False, action='store_true',
help="Whether to use BCE or not (default: no)")
# Validation Options
parser.add_argument("--val_on_trainset", action='store_true', default=False,
help="enable validation on train set (default: False)")
parser.add_argument("--crop_val", action='store_false', default=True,
help='do crop for validation (default: True)')
# Logging Options
parser.add_argument("--logdir", type=str, default='./logs',
help="path to Log directory (default: ./logs)")
parser.add_argument("--name", type=str, default='Experiment',
help="name of the experiment - to append to log directory (default: Experiment)")
parser.add_argument("--sample_num", type=int, default=0,
help='number of samples for visualization (default: 0)')
parser.add_argument("--debug", action='store_true', default=False,
help="verbose option")
parser.add_argument("--visualize", action='store_false', default=True,
help="visualization on tensorboard (def: Yes)")
parser.add_argument("--print_interval", type=int, default=10,
help="print interval of loss (default: 10)")
parser.add_argument("--val_interval", type=int, default=1,
help="epoch interval for eval (default: 1)")
# Model Options
parser.add_argument("--backbone", type=str, default='resnet101',
choices=['resnet50', 'resnet101'], help='backbone for the body (def: resnet50)')
parser.add_argument("--deeplab", type=str, default="v3",
choices=['v3', 'v2', 'none'], help='network head')
parser.add_argument("--output_stride", type=int, default=16,
choices=[8, 16], help='stride for the backbone (def: 16)')
parser.add_argument("--no_pretrained", action='store_true', default=False,
help='Whether to use pretrained or not (def: True)')
parser.add_argument("--norm_act", type=str, default="iabn_sync",
help='Which BN to use (def: iabn_sync')
parser.add_argument("--pooling", type=int, default=32,
help='pooling in ASPP for the validation phase (def: 32)')
parser.add_argument("--cosine", action='store_true', default=False,
help='Whether to use cosine classifier (def: False)')
# Test and Checkpoint options
parser.add_argument("--test", action='store_true', default=False,
help="Whether to train or test only (def: train and test)")
parser.add_argument("--ckpt", default=None, type=str,
help="path to trained model. Leave it None if you want to retrain your model")
parser.add_argument("--continue_ckpt", default=False, action='store_true',
help="Restart from the ckpt. Named taken automatically from method name.")
parser.add_argument("--ckpt_interval", type=int, default=1,
help="epoch interval for saving model (default: 1)")
# Parameters for Knowledge Distillation of ILTSS (https://arxiv.org/abs/1907.13372)
parser.add_argument("--freeze", action='store_true', default=False,
help="Use this to freeze the feature extractor in incremental steps")
parser.add_argument("--loss_de", type=float, default=0., # Distillation on Encoder
help="Set this hyperparameter to a value greater than "
"0 to enable distillation on Encoder (L2)")
parser.add_argument("--loss_kd", type=float, default=0., # Distillation on Output
help="Set this hyperparameter to a value greater than "
"0 to enable Knowlesge Distillation (Soft-CrossEntropy)")
# Arguments for ICaRL (from https://arxiv.org/abs/1611.07725)
parser.add_argument("--icarl", default=False, action='store_true',
help="If enable ICaRL or not (def is not)")
parser.add_argument("--icarl_bkg", action='store_true', default=False,
help="If use background from GT (def: No)")
# MiB
parser.add_argument("--init_balanced", default=False, action='store_true',
help="Enable Background-based initialization for new classes")
parser.add_argument("--unkd", default=False, action='store_true',
help="Enable Unbiased Knowledge Distillation instead of Knowledge Distillation")
parser.add_argument("--alpha", default=1., type=float,
help="The parameter to hard-ify the soft-labels. Def is 1.")
parser.add_argument("--unce", default=False, action='store_true',
help="Enable Unbiased Cross Entropy instead of CrossEntropy")
# # PLOP
parser.add_argument("--pod", default=None, type=str,
help="Enable POD distillation")
parser.add_argument("--pseudo", default=None, type=str,
help="Enable POD distillation")
# Incremental parameters
parser.add_argument("--task", type=str, default="19-1", choices=tasks.get_task_list(),
help="Task to be executed (default: 19-1)")
parser.add_argument("--step", type=int, default=0,
help="The incremental step in execution (default: 0)")
parser.add_argument("--no_mask", action='store_true', default=False,
help="Use this to not mask the old classes in new training set")
parser.add_argument("--overlap", action='store_true', default=False,
help="Use this to not use the new classes in the old training set")
parser.add_argument("--step_ckpt", default=None, type=str,
help="path to trained model at previous step. Leave it None if you want to use def path")
return parser