# pylint: disable=duplicate-code
"""Mask RCNN SHIFT training example."""
from __future__ import annotations
import lightning.pytorch as pl
from torch.optim import SGD
from torch.optim.lr_scheduler import LinearLR, MultiStepLR
from vis4d.config import FieldConfigDict, class_config
from vis4d.data.io.hdf5 import HDF5Backend
from vis4d.engine.callbacks import EvaluatorCallback, VisualizerCallback
from vis4d.engine.connectors import CallbackConnector, DataConnector
from vis4d.eval.shift import SHIFTDetectEvaluator
from vis4d.op.base import ResNet
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 import (
CONN_BBOX_2D_TEST,
CONN_BBOX_2D_TRAIN,
CONN_INS_MASK_2D_VIS,
)
from vis4d.zoo.base.datasets.shift import (
CONN_SHIFT_INS_EVAL,
get_shift_instance_seg_config,
)
from vis4d.zoo.base.models.mask_rcnn import get_mask_rcnn_cfg
[docs]
def get_config() -> FieldConfigDict:
"""Returns the Faster-RCNN config dict for the SHIFT detection task.
Returns:
FieldConfigDict: The configuration
"""
######################################################
## General Config ##
######################################################
config = get_default_cfg(exp_name="mask_rcnn_r50_12e_shift")
# High level hyper parameters
params = FieldConfigDict()
params.samples_per_gpu = 2
params.workers_per_gpu = 2
params.lr = 0.02
params.num_epochs = 12
params.num_classes = 6
config.params = params
######################################################
## Datasets with augmentations ##
######################################################
data_root = "data/shift/"
views_to_load = ["front"]
train_split = "train"
test_split = "val"
domain_attr = [{"weather_coarse": "clear", "timeofday_coarse": "daytime"}]
data_backend = class_config(HDF5Backend)
config.data = get_shift_instance_seg_config(
data_root=data_root,
train_split=train_split,
test_split=test_split,
train_views_to_load=views_to_load,
test_views_to_load=views_to_load,
train_attributes_to_load=domain_attr,
test_attributes_to_load=domain_attr,
data_backend=data_backend,
samples_per_gpu=params.samples_per_gpu,
workers_per_gpu=params.workers_per_gpu,
)
######################################################
## MODEL & LOSS ##
######################################################
basemodel = class_config(
ResNet, resnet_name="resnet50", pretrained=True, trainable_layers=4
)
config.model, config.loss = get_mask_rcnn_cfg(
num_classes=params.num_classes,
basemodel=basemodel,
no_overlap=True,
)
######################################################
## OPTIMIZERS ##
######################################################
config.optimizers = [
get_optimizer_cfg(
optimizer=class_config(
SGD, lr=params.lr, momentum=0.9, weight_decay=0.0001
),
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(MultiStepLR, milestones=[8, 11], gamma=0.1),
),
],
)
]
######################################################
## DATA CONNECTOR ##
######################################################
config.train_data_connector = class_config(
DataConnector, key_mapping=CONN_BBOX_2D_TRAIN
)
config.test_data_connector = class_config(
DataConnector, key_mapping=CONN_BBOX_2D_TEST
)
######################################################
## CALLBACKS ##
######################################################
# Logger and Checkpoint
callbacks = get_default_callbacks_cfg(config.output_dir)
# Visualizer
callbacks.append(
class_config(
VisualizerCallback,
visualizer=class_config(SegMaskVisualizer, vis_freq=25),
save_prefix=config.output_dir,
test_connector=class_config(
CallbackConnector, key_mapping=CONN_INS_MASK_2D_VIS
),
)
)
# Evaluator
callbacks.append(
class_config(
EvaluatorCallback,
evaluator=class_config(
SHIFTDetectEvaluator,
annotation_path=(
f"{data_root}/discrete/images/val/front/det_insseg_2d.json"
),
attributes_to_load=domain_attr,
),
test_connector=class_config(
CallbackConnector, key_mapping=CONN_SHIFT_INS_EVAL
),
metrics_to_eval=[
SHIFTDetectEvaluator.METRICS_DET,
SHIFTDetectEvaluator.METRICS_INS_SEG,
],
)
)
config.callbacks = callbacks
######################################################
## PL CLI ##
######################################################
# PL Trainer args
pl_trainer = get_default_pl_trainer_cfg(config)
pl_trainer.max_epochs = params.num_epochs
config.pl_trainer = pl_trainer
# PL Callbacks
pl_callbacks: list[pl.callbacks.Callback] = []
config.pl_callbacks = pl_callbacks
return config.value_mode()