# Copyright (c) OpenMMLab. All rights reserved.
from mmengine.config import read_base

with read_base():
    from mmpose.configs._base_.default_runtime import *  # noqa

from albumentations.augmentations import Blur, CoarseDropout, MedianBlur
from mmdet.engine.hooks import PipelineSwitchHook
from mmengine.dataset import DefaultSampler
from mmengine.hooks import EMAHook
from mmengine.model import PretrainedInit
from mmengine.optim import CosineAnnealingLR, LinearLR, OptimWrapper
from torch.nn import SiLU, SyncBatchNorm
from torch.optim import AdamW

from mmpose.codecs import SimCCLabel
from mmpose.datasets import (AicDataset, CocoWholeBodyDataset, COFWDataset,
                             CombinedDataset, CrowdPoseDataset,
                             Face300WDataset, GenerateTarget,
                             GetBBoxCenterScale, HalpeDataset,
                             HumanArt21Dataset, InterHand2DDoubleDataset,
                             JhmdbDataset, KeypointConverter, LapaDataset,
                             LoadImage, MpiiDataset, PackPoseInputs,
                             PoseTrack18Dataset, RandomFlip, RandomHalfBody,
                             TopdownAffine, UBody2dDataset, WFLWDataset)
from mmpose.datasets.transforms.common_transforms import (
    Albumentation, PhotometricDistortion, RandomBBoxTransform)
from mmpose.engine.hooks import ExpMomentumEMA
from mmpose.evaluation import CocoWholeBodyMetric
from mmpose.models import (CSPNeXt, CSPNeXtPAFPN, KLDiscretLoss,
                           PoseDataPreprocessor, RTMWHead,
                           TopdownPoseEstimator)

# common setting
num_keypoints = 133
input_size = (192, 256)

# runtime
max_epochs = 270
stage2_num_epochs = 10
base_lr = 5e-4
train_batch_size = 1024
val_batch_size = 32

train_cfg.update(max_epochs=max_epochs, val_interval=10)  # noqa
randomness = dict(seed=21)

# optimizer
optim_wrapper = dict(
    type=OptimWrapper,
    optimizer=dict(type=AdamW, lr=base_lr, weight_decay=0.1),
    clip_grad=dict(max_norm=35, norm_type=2),
    paramwise_cfg=dict(
        norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True))

# learning rate
param_scheduler = [
    dict(
        type=LinearLR, start_factor=1.0e-5, by_epoch=False, begin=0, end=1000),
    dict(
        type=CosineAnnealingLR,
        eta_min=base_lr * 0.05,
        begin=max_epochs // 2,
        end=max_epochs,
        T_max=max_epochs // 2,
        by_epoch=True,
        convert_to_iter_based=True),
]

# automatically scaling LR based on the actual training batch size
auto_scale_lr = dict(base_batch_size=8192)

# codec settings
codec = dict(
    type=SimCCLabel,
    input_size=input_size,
    sigma=(4.9, 5.66),
    simcc_split_ratio=2.0,
    normalize=False,
    use_dark=False)

# model settings
model = dict(
    type=TopdownPoseEstimator,
    data_preprocessor=dict(
        type=PoseDataPreprocessor,
        mean=[123.675, 116.28, 103.53],
        std=[58.395, 57.12, 57.375],
        bgr_to_rgb=True),
    backbone=dict(
        type=CSPNeXt,
        arch='P5',
        expand_ratio=0.5,
        deepen_factor=1.,
        widen_factor=1.,
        channel_attention=True,
        norm_cfg=dict(type='BN'),
        act_cfg=dict(type=SiLU),
        init_cfg=dict(
            type=PretrainedInit,
            prefix='backbone.',
            checkpoint='https://download.openmmlab.com/mmpose/v1/projects/'
            'rtmposev1/rtmpose-l_simcc-ucoco_dw-ucoco_270e-256x192-4d6dfc62_20230728.pth'  # noqa
        )),
    neck=dict(
        type=CSPNeXtPAFPN,
        in_channels=[256, 512, 1024],
        out_channels=None,
        out_indices=(
            1,
            2,
        ),
        num_csp_blocks=2,
        expand_ratio=0.5,
        norm_cfg=dict(type=SyncBatchNorm),
        act_cfg=dict(type=SiLU, inplace=True)),
    head=dict(
        type=RTMWHead,
        in_channels=1024,
        out_channels=num_keypoints,
        input_size=input_size,
        in_featuremap_size=tuple([s // 32 for s in input_size]),
        simcc_split_ratio=codec['simcc_split_ratio'],
        final_layer_kernel_size=7,
        gau_cfg=dict(
            hidden_dims=256,
            s=128,
            expansion_factor=2,
            dropout_rate=0.,
            drop_path=0.,
            act_fn='SiLU',
            use_rel_bias=False,
            pos_enc=False),
        loss=dict(
            type=KLDiscretLoss,
            use_target_weight=True,
            beta=1.,
            label_softmax=True,
            label_beta=10.,
            mask=list(range(23, 91)),
            mask_weight=0.5,
        ),
        decoder=codec),
    test_cfg=dict(flip_test=True))

# base dataset settings
dataset_type = CocoWholeBodyDataset
data_mode = 'topdown'
data_root = 'data/'

backend_args = dict(backend='local')

# pipelines
train_pipeline = [
    dict(type=LoadImage, backend_args=backend_args),
    dict(type=GetBBoxCenterScale),
    dict(type=RandomFlip, direction='horizontal'),
    dict(type=RandomHalfBody),
    dict(type=RandomBBoxTransform, scale_factor=[0.5, 1.5], rotate_factor=90),
    dict(type=TopdownAffine, input_size=codec['input_size']),
    dict(type=PhotometricDistortion),
    dict(
        type=Albumentation,
        transforms=[
            dict(type=Blur, p=0.1),
            dict(type=MedianBlur, p=0.1),
            dict(
                type=CoarseDropout,
                max_holes=1,
                max_height=0.4,
                max_width=0.4,
                min_holes=1,
                min_height=0.2,
                min_width=0.2,
                p=0.5),
        ]),
    dict(
        type=GenerateTarget, encoder=codec, use_dataset_keypoint_weights=True),
    dict(type=PackPoseInputs)
]
val_pipeline = [
    dict(type=LoadImage, backend_args=backend_args),
    dict(type=GetBBoxCenterScale),
    dict(type=TopdownAffine, input_size=codec['input_size']),
    dict(type=PackPoseInputs)
]
train_pipeline_stage2 = [
    dict(type=LoadImage, backend_args=backend_args),
    dict(type=GetBBoxCenterScale),
    dict(type=RandomFlip, direction='horizontal'),
    dict(type=RandomHalfBody),
    dict(
        type=RandomBBoxTransform,
        shift_factor=0.,
        scale_factor=[0.5, 1.5],
        rotate_factor=90),
    dict(type=TopdownAffine, input_size=codec['input_size']),
    dict(
        type=Albumentation,
        transforms=[
            dict(type=Blur, p=0.1),
            dict(type=MedianBlur, p=0.1),
        ]),
    dict(
        type=GenerateTarget, encoder=codec, use_dataset_keypoint_weights=True),
    dict(type=PackPoseInputs)
]

# mapping

aic_coco133 = [(0, 6), (1, 8), (2, 10), (3, 5), (4, 7), (5, 9), (6, 12),
               (7, 14), (8, 16), (9, 11), (10, 13), (11, 15)]

crowdpose_coco133 = [(0, 5), (1, 6), (2, 7), (3, 8), (4, 9), (5, 10), (6, 11),
                     (7, 12), (8, 13), (9, 14), (10, 15), (11, 16)]

mpii_coco133 = [
    (0, 16),
    (1, 14),
    (2, 12),
    (3, 11),
    (4, 13),
    (5, 15),
    (10, 10),
    (11, 8),
    (12, 6),
    (13, 5),
    (14, 7),
    (15, 9),
]

jhmdb_coco133 = [
    (3, 6),
    (4, 5),
    (5, 12),
    (6, 11),
    (7, 8),
    (8, 7),
    (9, 14),
    (10, 13),
    (11, 10),
    (12, 9),
    (13, 16),
    (14, 15),
]

halpe_coco133 = [(i, i)
                 for i in range(17)] + [(20, 17), (21, 20), (22, 18), (23, 21),
                                        (24, 19),
                                        (25, 22)] + [(i, i - 3)
                                                     for i in range(26, 136)]

posetrack_coco133 = [
    (0, 0),
    (3, 3),
    (4, 4),
    (5, 5),
    (6, 6),
    (7, 7),
    (8, 8),
    (9, 9),
    (10, 10),
    (11, 11),
    (12, 12),
    (13, 13),
    (14, 14),
    (15, 15),
    (16, 16),
]

humanart_coco133 = [(i, i) for i in range(17)] + [(17, 99), (18, 120),
                                                  (19, 17), (20, 20)]

# train datasets
dataset_coco = dict(
    type=dataset_type,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='coco/annotations/coco_wholebody_train_v1.0.json',
    data_prefix=dict(img='detection/coco/train2017/'),
    pipeline=[],
)

dataset_aic = dict(
    type=AicDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='aic/annotations/aic_train.json',
    data_prefix=dict(img='pose/ai_challenge/ai_challenger_keypoint'
                     '_train_20170902/keypoint_train_images_20170902/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=aic_coco133)
    ],
)

dataset_crowdpose = dict(
    type=CrowdPoseDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='crowdpose/annotations/mmpose_crowdpose_trainval.json',
    data_prefix=dict(img='pose/CrowdPose/images/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=crowdpose_coco133)
    ],
)

dataset_mpii = dict(
    type=MpiiDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='mpii/annotations/mpii_train.json',
    data_prefix=dict(img='pose/MPI/images/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=mpii_coco133)
    ],
)

dataset_jhmdb = dict(
    type=JhmdbDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='jhmdb/annotations/Sub1_train.json',
    data_prefix=dict(img='pose/JHMDB/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=jhmdb_coco133)
    ],
)

dataset_halpe = dict(
    type=HalpeDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='halpe/annotations/halpe_train_v1.json',
    data_prefix=dict(img='pose/Halpe/hico_20160224_det/images/train2015'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=halpe_coco133)
    ],
)

dataset_posetrack = dict(
    type=PoseTrack18Dataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='posetrack18/annotations/posetrack18_train.json',
    data_prefix=dict(img='pose/PoseChallenge2018/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=posetrack_coco133)
    ],
)

dataset_humanart = dict(
    type=HumanArt21Dataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='HumanArt/annotations/training_humanart.json',
    filter_cfg=dict(scenes=['real_human']),
    data_prefix=dict(img='pose/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=humanart_coco133)
    ])

ubody_scenes = [
    'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow',
    'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing',
    'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference'
]

ubody_datasets = []
for scene in ubody_scenes:
    each = dict(
        type=UBody2dDataset,
        data_root=data_root,
        data_mode=data_mode,
        ann_file=f'Ubody/annotations/{scene}/train_annotations.json',
        data_prefix=dict(img='pose/UBody/images/'),
        pipeline=[],
        sample_interval=10)
    ubody_datasets.append(each)

dataset_ubody = dict(
    type=CombinedDataset,
    metainfo=dict(from_file='configs/_base_/datasets/ubody2d.py'),
    datasets=ubody_datasets,
    pipeline=[],
    test_mode=False,
)

face_pipeline = [
    dict(type=LoadImage, backend_args=backend_args),
    dict(type=GetBBoxCenterScale, padding=1.25),
    dict(
        type=RandomBBoxTransform,
        shift_factor=0.,
        scale_factor=[1.5, 2.0],
        rotate_factor=0),
]

wflw_coco133 = [(i * 2, 23 + i)
                for i in range(17)] + [(33 + i, 40 + i) for i in range(5)] + [
                    (42 + i, 45 + i) for i in range(5)
                ] + [(51 + i, 50 + i)
                     for i in range(9)] + [(60, 59), (61, 60), (63, 61),
                                           (64, 62), (65, 63), (67, 64),
                                           (68, 65), (69, 66), (71, 67),
                                           (72, 68), (73, 69),
                                           (75, 70)] + [(76 + i, 71 + i)
                                                        for i in range(20)]
dataset_wflw = dict(
    type=WFLWDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='wflw/annotations/face_landmarks_wflw_train.json',
    data_prefix=dict(img='pose/WFLW/images/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=wflw_coco133), *face_pipeline
    ],
)

mapping_300w_coco133 = [(i, 23 + i) for i in range(68)]
dataset_300w = dict(
    type=Face300WDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='300w/annotations/face_landmarks_300w_train.json',
    data_prefix=dict(img='pose/300w/images/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=mapping_300w_coco133), *face_pipeline
    ],
)

cofw_coco133 = [(0, 40), (2, 44), (4, 42), (1, 49), (3, 45), (6, 47), (8, 59),
                (10, 62), (9, 68), (11, 65), (18, 54), (19, 58), (20, 53),
                (21, 56), (22, 71), (23, 77), (24, 74), (25, 85), (26, 89),
                (27, 80), (28, 31)]
dataset_cofw = dict(
    type=COFWDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='cofw/annotations/cofw_train.json',
    data_prefix=dict(img='pose/COFW/images/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=cofw_coco133), *face_pipeline
    ],
)

lapa_coco133 = [(i * 2, 23 + i) for i in range(17)] + [
    (33 + i, 40 + i) for i in range(5)
] + [(42 + i, 45 + i) for i in range(5)] + [
    (51 + i, 50 + i) for i in range(4)
] + [(58 + i, 54 + i) for i in range(5)] + [(66, 59), (67, 60), (69, 61),
                                            (70, 62), (71, 63), (73, 64),
                                            (75, 65), (76, 66), (78, 67),
                                            (79, 68), (80, 69),
                                            (82, 70)] + [(84 + i, 71 + i)
                                                         for i in range(20)]
dataset_lapa = dict(
    type=LapaDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='LaPa/annotations/lapa_trainval.json',
    data_prefix=dict(img='pose/LaPa/'),
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=lapa_coco133), *face_pipeline
    ],
)

dataset_wb = dict(
    type=CombinedDataset,
    metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
    datasets=[dataset_coco, dataset_halpe, dataset_ubody],
    pipeline=[],
    test_mode=False,
)

dataset_body = dict(
    type=CombinedDataset,
    metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
    datasets=[
        dataset_aic,
        dataset_crowdpose,
        dataset_mpii,
        dataset_jhmdb,
        dataset_posetrack,
        dataset_humanart,
    ],
    pipeline=[],
    test_mode=False,
)

dataset_face = dict(
    type=CombinedDataset,
    metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
    datasets=[
        dataset_wflw,
        dataset_300w,
        dataset_cofw,
        dataset_lapa,
    ],
    pipeline=[],
    test_mode=False,
)

hand_pipeline = [
    dict(type=LoadImage, backend_args=backend_args),
    dict(type=GetBBoxCenterScale),
    dict(
        type=RandomBBoxTransform,
        shift_factor=0.,
        scale_factor=[1.5, 2.0],
        rotate_factor=0),
]

interhand_left = [(21, 95), (22, 94), (23, 93), (24, 92), (25, 99), (26, 98),
                  (27, 97), (28, 96), (29, 103), (30, 102), (31, 101),
                  (32, 100), (33, 107), (34, 106), (35, 105), (36, 104),
                  (37, 111), (38, 110), (39, 109), (40, 108), (41, 91)]
interhand_right = [(i - 21, j + 21) for i, j in interhand_left]
interhand_coco133 = interhand_right + interhand_left

dataset_interhand2d = dict(
    type=InterHand2DDoubleDataset,
    data_root=data_root,
    data_mode=data_mode,
    ann_file='interhand26m/annotations/all/InterHand2.6M_train_data.json',
    camera_param_file='interhand26m/annotations/all/'
    'InterHand2.6M_train_camera.json',
    joint_file='interhand26m/annotations/all/'
    'InterHand2.6M_train_joint_3d.json',
    data_prefix=dict(img='interhand2.6m/images/train/'),
    sample_interval=10,
    pipeline=[
        dict(
            type=KeypointConverter,
            num_keypoints=num_keypoints,
            mapping=interhand_coco133,
        ), *hand_pipeline
    ],
)

dataset_hand = dict(
    type=CombinedDataset,
    metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
    datasets=[dataset_interhand2d],
    pipeline=[],
    test_mode=False,
)

train_datasets = [dataset_wb, dataset_body, dataset_face, dataset_hand]

# data loaders
train_dataloader = dict(
    batch_size=train_batch_size,
    num_workers=4,
    pin_memory=False,
    persistent_workers=True,
    sampler=dict(type=DefaultSampler, shuffle=True),
    dataset=dict(
        type=CombinedDataset,
        metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'),
        datasets=train_datasets,
        pipeline=train_pipeline,
        test_mode=False,
    ))

val_dataloader = dict(
    batch_size=val_batch_size,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type=DefaultSampler, shuffle=False, round_up=False),
    dataset=dict(
        type=CocoWholeBodyDataset,
        ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json',
        data_prefix=dict(img='data/detection/coco/val2017/'),
        pipeline=val_pipeline,
        bbox_file='data/coco/person_detection_results/'
        'COCO_val2017_detections_AP_H_56_person.json',
        test_mode=True))

test_dataloader = val_dataloader

# hooks
default_hooks.update(  # noqa
    checkpoint=dict(
        save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1))

custom_hooks = [
    dict(
        type=EMAHook,
        ema_type=ExpMomentumEMA,
        momentum=0.0002,
        update_buffers=True,
        priority=49),
    dict(
        type=PipelineSwitchHook,
        switch_epoch=max_epochs - stage2_num_epochs,
        switch_pipeline=train_pipeline_stage2)
]

# evaluators
val_evaluator = dict(
    type=CocoWholeBodyMetric,
    ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json')
test_evaluator = val_evaluator
