cascade_internimage_l_fpn_1x_coco.py

# --------------------------------------------------------
# InternImage
# Copyright (c) 2022 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------
_base_ = [
    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
    '../_base_/datasets/coco_instance.py',
    '../_base_/schedules/schedule_1x.py',
    '../_base_/default_runtime.py'
]
pretrained = 'https://huggingface.co/OpenGVLab/InternImage/resolve/main/internimage_l_22k_192to384.pth'
model = dict(
    backbone=dict(
        _delete_=True,
        type='InternImage',
        core_op='DCNv3',
        channels=160,
        depths=[5, 5, 22, 5],
        groups=[10, 20, 40, 80],
        mlp_ratio=4.,
        drop_path_rate=0.4,
        norm_layer='LN',
        layer_scale=1.0,
        offset_scale=2.0,
        post_norm=True,
        with_cp=False,
        out_indices=(0, 1, 2, 3),
        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
    neck=dict(
        type='FPN',
        in_channels=[160, 320, 640, 1280],
        out_channels=256,
        num_outs=5),
    roi_head=dict(
        bbox_head=[
            dict(
                type='ConvFCBBoxHead',
                num_shared_convs=4,
                num_shared_fcs=1,
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.1, 0.1, 0.2, 0.2]),
                reg_class_agnostic=False,
                reg_decoded_bbox=True,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                loss_cls=dict(
                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
            dict(
                type='ConvFCBBoxHead',
                num_shared_convs=4,
                num_shared_fcs=1,
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.05, 0.05, 0.1, 0.1]),
                reg_class_agnostic=False,
                reg_decoded_bbox=True,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                loss_cls=dict(
                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
            dict(
                type='ConvFCBBoxHead',
                num_shared_convs=4,
                num_shared_fcs=1,
                in_channels=256,
                conv_out_channels=256,
                fc_out_channels=1024,
                roi_feat_size=7,
                num_classes=80,
                bbox_coder=dict(
                    type='DeltaXYWHBBoxCoder',
                    target_means=[0., 0., 0., 0.],
                    target_stds=[0.033, 0.033, 0.067, 0.067]),
                reg_class_agnostic=False,
                reg_decoded_bbox=True,
                norm_cfg=dict(type='SyncBN', requires_grad=True),
                loss_cls=dict(
                    type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
                loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
]))
# By default, models are trained on 8 GPUs with 2 images per GPU
data = dict(samples_per_gpu=2)
optimizer = dict(
    _delete_=True, type='AdamW', lr=0.0001, weight_decay=0.05,
    constructor='CustomLayerDecayOptimizerConstructor',
    paramwise_cfg=dict(num_layers=37, layer_decay_rate=0.94,
                       depths=[5, 5, 22, 5]))
optimizer_config = dict(grad_clip=None)
# fp16 = dict(loss_scale=dict(init_scale=512))
evaluation = dict(save_best='auto')
checkpoint_config = dict(
    interval=1,
    max_keep_ckpts=3,
    save_last=True,
)