# --------------------------------------------------------
# InternImage
# Copyright (c) 2022 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
_base_ = [
    '../_base_/models/mask2former_beit.py', '../_base_/datasets/coco-stuff10k.py',
    '../_base_/default_runtime.py', '../_base_/schedules/schedule_40k.py'
]
num_classes = 171
load_from = 'https://huggingface.co/OpenGVLab/InternImage/resolve/main/mask2former_internimage_h_896_80k_cocostuff164k.pth'
model = dict(
    type='EncoderDecoderMask2Former',
    backbone=dict(
        _delete_=True,
        type='InternImage',
        core_op='DCNv3',
        channels=320,
        depths=[6, 6, 32, 6],
        groups=[10, 20, 40, 80],
        mlp_ratio=4.,
        drop_path_rate=0.5,
        norm_layer='LN',
        layer_scale=None,
        offset_scale=1.0,
        post_norm=False,
        dw_kernel_size=5, # for InternImage-H/G
        res_post_norm=True, # for InternImage-H/G
        level2_post_norm=True, # for InternImage-H/G
        level2_post_norm_block_ids=[5, 11, 17, 23, 29], # for InternImage-H/G
        center_feature_scale=True, # for InternImage-H/G
        with_cp=False,
        out_indices=(0, 1, 2, 3),
        init_cfg=None),
    decode_head=dict(
        in_channels=[320, 640, 1280, 2560],
        feat_channels=1024,
        out_channels=1024,
        num_classes=num_classes,
        num_queries=200,
        pixel_decoder=dict(
            type='MSDeformAttnPixelDecoder',
            num_outs=3,
            norm_cfg=dict(type='GN', num_groups=32),
            act_cfg=dict(type='ReLU'),
            encoder=dict(
                type='DetrTransformerEncoder',
                num_layers=6,
                transformerlayers=dict(
                    type='BaseTransformerLayer',
                    attn_cfgs=dict(
                        type='MultiScaleDeformableAttention',
                        embed_dims=1024,
                        num_heads=32,
                        num_levels=3,
                        num_points=4,
                        im2col_step=64,
                        dropout=0.0,
                        batch_first=False,
                        norm_cfg=None,
                        init_cfg=None),
                    ffn_cfgs=dict(
                        type='FFN',
                        embed_dims=1024,
                        feedforward_channels=4096,
                        num_fcs=2,
                        ffn_drop=0.0,
                        with_cp=False,  # set with_cp=True to save memory
                        act_cfg=dict(type='ReLU', inplace=True)),
                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),
                init_cfg=None),
            positional_encoding=dict(
                type='SinePositionalEncoding', num_feats=512, normalize=True),
            init_cfg=None),
        positional_encoding=dict(
            type='SinePositionalEncoding', num_feats=512, normalize=True),
        transformer_decoder=dict(
            type='DetrTransformerDecoder',
            return_intermediate=True,
            num_layers=9,
            transformerlayers=dict(
                type='DetrTransformerDecoderLayer',
                attn_cfgs=dict(
                    type='MultiheadAttention',
                    embed_dims=1024,
                    num_heads=32,
                    attn_drop=0.0,
                    proj_drop=0.0,
                    dropout_layer=None,
                    batch_first=False),
                ffn_cfgs=dict(
                    embed_dims=1024,
                    feedforward_channels=4096,
                    num_fcs=2,
                    act_cfg=dict(type='ReLU', inplace=True),
                    ffn_drop=0.0,
                    dropout_layer=None,
                    with_cp=False,  # set with_cp=True to save memory
                    add_identity=True),
                feedforward_channels=4096,
                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
                                 'ffn', 'norm')),
            init_cfg=None),
        loss_cls=dict(
            type='CrossEntropyLoss',
            use_sigmoid=False,
            loss_weight=2.0,
            reduction='mean',
            class_weight=[1.0] * num_classes + [0.1])
    ),
    test_cfg=dict(mode='whole'))
crop_size = (512, 512)
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(type='LoadAnnotations', reduce_zero_label=True),
    dict(type='Resize', img_scale=(2048, 512), ratio_range=(0.5, 2.0)),
    dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
    dict(type='RandomFlip', prob=0.5),
    dict(type='PhotoMetricDistortion'),
    dict(type='Normalize', **img_norm_cfg),
    dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
    dict(type='ToMask'),
    dict(type='DefaultFormatBundle'),
    dict(type='Collect', keys=['img', 'gt_semantic_seg', 'gt_masks', 'gt_labels'])
]
test_pipeline = [
    dict(type='LoadImageFromFile'),
    dict(
        type='MultiScaleFlipAug',
        img_scale=(2048, 512),
        # img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
        flip=False,
        transforms=[
            dict(type='Resize', keep_ratio=True),
            dict(type='ResizeToMultiple', size_divisor=32),
            dict(type='RandomFlip'),
            dict(type='Normalize', **img_norm_cfg),
            dict(type='ImageToTensor', keys=['img']),
            dict(type='Collect', keys=['img']),
        ])
]
optimizer = dict(
    _delete_=True, type='AdamW', lr=1e-5, betas=(0.9, 0.999), weight_decay=0.05,
    constructor='CustomLayerDecayOptimizerConstructor',
    paramwise_cfg=dict(num_layers=50, layer_decay_rate=0.95,
                       depths=[6, 6, 32, 6], offset_lr_scale=1.0))
lr_config = dict(_delete_=True, policy='poly',
                 warmup='linear',
                 warmup_iters=1500,
                 warmup_ratio=1e-6,
                 power=1.0, min_lr=0.0, by_epoch=False)
# By default, models are trained on 16 GPUs with 1 images per GPU
data = dict(samples_per_gpu=1,
            train=dict(pipeline=train_pipeline),
            val=dict(pipeline=test_pipeline),
            test=dict(pipeline=test_pipeline))
runner = dict(type='IterBasedRunner')
checkpoint_config = dict(by_epoch=False, interval=1000, max_keep_ckpts=1)
evaluation = dict(interval=2000, metric='mIoU', save_best='mIoU')
# fp16 = dict(loss_scale=dict(init_scale=512))
