# pylint: disable=duplicate-code
"""Semantic FPN BDD100K training example."""
from __future__ import annotations
import lightning.pytorch as pl
from torch.optim import SGD
from torch.optim.lr_scheduler import LinearLR
from vis4d.config import class_config
from vis4d.config.typing import ExperimentConfig, ExperimentParameters
from vis4d.data.io.hdf5 import HDF5Backend
from vis4d.engine.callbacks import EvaluatorCallback, VisualizerCallback
from vis4d.engine.connectors import (
CallbackConnector,
DataConnector,
LossConnector,
)
from vis4d.engine.loss_module import LossModule
from vis4d.engine.optim import PolyLR
from vis4d.eval.bdd100k import BDD100KSegEvaluator
from vis4d.model.seg.semantic_fpn import SemanticFPN
from vis4d.op.loss import SegCrossEntropyLoss
from vis4d.vis.image import SegMaskVisualizer
from vis4d.zoo.base import (
get_default_callbacks_cfg,
get_default_cfg,
get_default_pl_trainer_cfg,
get_lr_scheduler_cfg,
get_optimizer_cfg,
)
from vis4d.zoo.base.data_connectors.seg import (
CONN_MASKS_TEST,
CONN_MASKS_TRAIN,
CONN_SEG_LOSS,
CONN_SEG_VIS,
)
from vis4d.zoo.base.datasets.bdd100k import (
CONN_BDD100K_SEG_EVAL,
get_bdd100k_sem_seg_cfg,
)
[docs]
def get_config() -> ExperimentConfig:
"""Returns the config dict for the BDD100K semantic segmentation task.
Returns:
ExperimentConfig: The configuration
"""
######################################################
## General Config ##
######################################################
config = get_default_cfg(exp_name="semantic_fpn_r50_80k_bdd100k")
config.sync_batchnorm = True
config.val_check_interval = 4000
config.check_val_every_n_epoch = None
## High level hyper parameters
params = ExperimentParameters()
params.samples_per_gpu = 2
params.workers_per_gpu = 2
params.lr = 0.01
params.num_steps = 80000
params.num_classes = 19
config.params = params
######################################################
## Datasets with augmentations ##
######################################################
data_root = "data/bdd100k/images/10k"
train_split = "train"
test_split = "val"
data_backend = class_config(HDF5Backend)
config.data = get_bdd100k_sem_seg_cfg(
data_root=data_root,
train_split=train_split,
test_split=test_split,
data_backend=data_backend,
samples_per_gpu=params.samples_per_gpu,
workers_per_gpu=params.workers_per_gpu,
)
######################################################
## MODEL & LOSS ##
######################################################
config.model = class_config(SemanticFPN, num_classes=params.num_classes)
config.loss = class_config(
LossModule,
losses=[
{
"loss": class_config(SegCrossEntropyLoss),
"connector": class_config(
LossConnector, key_mapping=CONN_SEG_LOSS
),
},
],
)
######################################################
## OPTIMIZERS ##
######################################################
config.optimizers = [
get_optimizer_cfg(
optimizer=class_config(
SGD, lr=params.lr, momentum=0.9, weight_decay=0.0005
),
lr_schedulers=[
get_lr_scheduler_cfg(
class_config(
LinearLR, start_factor=0.001, total_iters=500
),
end=500,
epoch_based=False,
),
get_lr_scheduler_cfg(
class_config(
PolyLR,
max_steps=params.num_steps,
min_lr=0.0001,
power=0.9,
),
epoch_based=False,
),
],
)
]
######################################################
## DATA CONNECTOR ##
######################################################
config.train_data_connector = class_config(
DataConnector, key_mapping=CONN_MASKS_TRAIN
)
config.test_data_connector = class_config(
DataConnector, key_mapping=CONN_MASKS_TEST
)
######################################################
## CALLBACKS ##
######################################################
callbacks = get_default_callbacks_cfg(
config.output_dir,
epoch_based=False,
checkpoint_period=config.val_check_interval,
)
# Evaluator
callbacks.append(
class_config(
EvaluatorCallback,
evaluator=class_config(
BDD100KSegEvaluator,
annotation_path="data/bdd100k/labels/sem_seg_val_rle.json",
),
test_connector=class_config(
CallbackConnector, key_mapping=CONN_BDD100K_SEG_EVAL
),
)
)
# Visualizer
callbacks.append(
class_config(
VisualizerCallback,
visualizer=class_config(SegMaskVisualizer, vis_freq=20),
save_prefix=config.output_dir,
test_connector=class_config(
CallbackConnector, key_mapping=CONN_SEG_VIS
),
)
)
config.callbacks = callbacks
######################################################
## PL CLI ##
######################################################
# PL Trainer args
pl_trainer = get_default_pl_trainer_cfg(config)
pl_trainer.epoch_based = False
pl_trainer.max_steps = params.num_steps
pl_trainer.checkpoint_period = config.val_check_interval
pl_trainer.val_check_interval = config.val_check_interval
pl_trainer.check_val_every_n_epoch = config.check_val_every_n_epoch
pl_trainer.sync_batchnorm = config.sync_batchnorm
# pl_trainer.precision = 16
config.pl_trainer = pl_trainer
# PL Callbacks
pl_callbacks: list[pl.callbacks.Callback] = []
config.pl_callbacks = pl_callbacks
return config.value_mode()